SARDet-100K: Towards Open-Source Benchmark and ToolKit for Large-Scale SAR Object Detection
Authors: Yuxuan Li
Xiang Li
Weijie Li
Qibin Hou
Li Liu
Ming-Ming Cheng
Jian Yang
Venue: NeurIPS 2024 spotlight (2024)
Keywords: Benchmark
Computer Vision
Object Detection
Synthetic Aperture Radar
Pretrain
Domain Transfer
Primary area: machine_vision
TL;DR: This paper presents a new benchmark for large-scale SAR object detection, introducing the SARDet-100k dataset and the Multi-Stage with Filter Augmentation (MSFA) pretrain method.

Abstract: Synthetic Aperture Radar (SAR) object detection has gained significant attention recently due to its irreplaceable all-weather imaging capabilities. However, this research field suffers from both limited public datasets (mostly comprising <2K images with only mono-category objects) and inaccessible source code. To tackle these challenges, we establish a new benchmark dataset and an open-source method for large-scale SAR object detection. Our dataset, SARDet-100K, is a result of intense surveying, collecting, and standardizing 10 existing SAR detection datasets, providing a large-scale and diverse dataset for research purposes. To the best of our knowledge, SARDet-100K is the first COCO-level large-scale multi-class SAR object detection dataset ever created. With this high-quality dataset, we conducted comprehensive experiments and uncovered a crucial challenge in SAR object detection: the substantial disparities between the pretraining on RGB datasets and finetuning on SAR datasets in terms of both data domain and model structure. To bridge these gaps, we propose a novel Multi-Stage with Filter Augmentation (MSFA) pretraining framework that tackles the problems from the perspective of data input, domain transition, and model migration. The proposed MSFA method significantly enhances the performance of SAR object detection models while demonstrating exceptional generalizability and flexibility across diverse models. This work aims to pave the way for further advancements in SAR object detection. The dataset and code is available at \url{https://github.com/zcablii/SARDet_100K}.

SARDet-100K3 Towards Open-Source Benchmark and ToolKit for Large-Scale SAR Object Detection
Yuxuan Li
Xiang Li li Weijie Li
Qibin Hou 1.2
Li Liu
Ming-Ming Cheng
Jian Yang!
PCA Lab, VCIP, CS, Nankai University
NKIARI, Futian, Shenzhen
AAcademy of Advanced Technology Research of Hunan
Corresponding Authors
yuxuan. 1i. 10..cl.lll... ac.uk, uk, {xiang 1i. liing.l..mmplus. ,oouh,, cmm, oaaaang) @nankai edu.
Abstract
Synthetic Aperture Radar (SAR) object detection has gained significant attention recently due its irreplaceable all-weather imaging capabilities. However, this research field suffers from both limited public datasets (mostly comprising <2K images with only mono-category objects) and inaccessible source code. To tackle these challenges, establish new benchmark dataset and an open-source method for large-scale SAR object detection. Our dataset, SARDet- 100K, a result intense surveying, collecting, and standardizing 10 existing SAR detection datasets, providing a scale and diverse dataset for research purposes. To the best knowledge, SARDet- -OOK is the first COCO-level large-scale multi-class SAR object detection dataset ever created. With this high-quality dataset, we conducted comprehensive experiments and uncovered crucial challenge in SAR object detection: the substantial disparities between the pretraining on RGB datasets and finetuning on SAR datasets in terms of both data domain and model structure. To bridge these gaps, we propose a novel Multi-Stage with Filter Augmentation (MSFA) pretraining framework that tackles the problems from the perspective of data input, domain transition, and model migration. The proposed MSFA method significantly enhances the performance of SAR object detection models while demonstrating exellent generalizability and flexibility across diverse models. This work aims to pave the way for further advancements in SAR object detection. The dataset and code is available at wthhtps: //ggtttt...t////////////gg com./zcabii//SAADeet _10K
Introduction
Synthetic Aperture Radar (SAR) [57 60] a pivotal technology in remote sensing, providing numer- advantages over traditional optical sensors. Notably, SAR possesses the capability acquire geographical images under any weather conditions, irrespective factors such as sunlight, land cover, certain types of camouflage. demonstrated in Fig. aa). As consequence of these ad- vantages, SAR has found extensive applications critical domains, including national defence (48)
humanitarian relief 3 68 camouflage detection [9]] and geological exploration [51 24
With its invaluable benefits, the field of SAR object detection has garnered increasing attention. In recent years, there has been substantial increase in the number of research papers focusing this field, as illustrated in Fig. [b). Despite the increasing influence, this research area has suffered from significant challenges including limited resources and transferring gaps.
Limited resources. significant obstacle in nigh-resolution SAR image object detection is the sensitivity of SAR images, coupled with the high costs associated with annotating these images. This
38th Conference on Neural Information Processing Systems (NcurIPS 2024).

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Num, of papers (K)
60
60
2023
RGB
2022
2021
50.6
2020
47.5
2019
42.3
2018
27.3
SAR
2017
20.0
2016
30.5
2015
29.2
Night
Cloud
Snow
Land cover
2014
212
(b)
Figure 1:
(a) Advantages of SAR image: independent of weather conditions, sunlight and land cover.
(b) Number of papers (thousands) retrieved from Google Scholar using keywords "SAR Detection".
severely restricts the availability of public datasets. Existing datasets, such as SAR-AIRcraft [90], Air- SARShip (76). SSDD [84), and HRSID (7T), typically consist of singular type of object against a simplistic background. Moreover, these datasets generally limited in scale, potentially introducing bias when evaluating different methodologies. Additionally, a notable barrier to advancing research in SAR object detection is the lack of publicly accessible source code, making it challenging to reproduce previous research findings and conduct fair comparisons or build upon existing work.
To address this problem, we merge the most publicly available SAR detection datasets. This effort includes comprehensive review of current public SAR detection resources, followed by the collection and standardization of these datasets into uniform format, creating unified large-scale multi-class dataset for SAR object detection, named SARDet- 100k. This dataset comprises approximately 1 117k images and 246k instances of objects across six distinct categories. To our knowledge, SARDet-100K the first dataset of COCO-scale magnitude in this research area. It significantly contributes to overcoming the previously mentioned limitations by providing rich resource for the development and evaluation of SAR object detection models. Moreover, the datasct and source code will be made publicly available.
Transferring gaps. Through our empirical research and detailed analysis, we have identified that a principal hurdle in SAR object detection is the significant domain gap and model gap encountered when transferring a backbone network pretrained on natural RGB datasets (e.g., ImageNet (17). to detection network on SAR imagery. The domain gap stems from the stark visual discrepancies between RGB and SAR imagery, whereas the model gap arises from the model differences between the pretrained backbone and the whole detection framework employed in the downstream task.
To mitigate the aforementioned domain gap and model gap, we propose novel Multi-Stage with Filter Augmentation (MSFA) pretraining framework to bridge these gaps. This framework addresses the challenge from multiple angles: data input, domain transition, and model migration, each tailored to the unique properties of the SAR image detection task. For data input: to address the input domain gap between the pretrain and finetune datasets, we employ traditional, handerafted feature descriptors. These descriptors e efficiently transform the input data from pixel space to feature space that is not only robust noise but also statistically narrows the gap between data from RGB and SAR modalities (see Fig. a))). thereby enhancing the transferability of pretrained knowledge. For domain transition: we propose a domain transition bridge utilizing an optical remote sensing detection dataset. This bridge connects natural RGB images through optics correlation and SAR images through object correlation, establishing a hierarchical pretraining approach that effectively closes the domain gap between RGB and SAR imagery (see Fig. 2 b)). For model migration: guarantee thorough training of the entire detection framework and to facilitate complete model migration for finetuning, we employ the entire detector bridging model throughout the multi-stage pretraining process.
The MSFA framework demonstrates remarkable efficacy in reducing the substantial domain and model gaps typically encountered between the pretraining and finetuning stages. MSFA is not only effective but also general and applicable across various modern deep neural networks.

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Our contribution to the field of SAR object detection can be concluded into the following FOUR points:
Introduction of the first COCO-level large-scale dataset for SAR multi- object detection.
Identification of critical gaps in traditional model pretrain and finetune approaches for SAR object detection.
Proposal of Multi-Stage with Filter Augmentation (MSFA) pretraining framework, which demonstrates remarkable effectiveness, as well as excellent generalizability and flexibility across various deep network models.
Establishment of a new benchmark in SAR object detection by releasing the datasets and code associated with our research. This contribution expected to foster further advancements and progress in the field.
2 Related Work
2.1 SAR Images and Handcraft Features
SAR imaging often suffers from poor image quality due to multiplicative speckle noise and arti- facts [5].47). To mitigate this, many traditional handcrafted feature descriptors have been developed or adapted to extract more discernible features from SAR images. These include Histogram of
Oriented Gradients
Haar- like 62 Feature Descriptor and Wavelet Scattering Transform 455 (WST). Early works employed
[15] (HOG), Canny Edge Detector 5). Gradient by Ratio Edge (GRE)
traditional algorithms, such as HOG for SAR object recognition [56] 49 Canny (28, 38 for edge
detection. However, in recent years, the field of SAR image analysis has been largely dominated by deep learning approaches.
While recent studies have focused on tasks related to low-level processing 66 86 classification S3 93
50 78 and pretrain 29 29), they have attempted to integrate classic handcrafted features into modern neural networks for robust SAR image feature extraction and refinement. In contrast, our work does not simply inject such handcrafted features into networks, but explores the benefits and potentials of handcrafted features in domain adaptation and SAR object detection under modern deep neural networks. This research area remains largely unexplored, and our work aims to bridge this gap.
85
26
2.2 SAR Object Detection
Various popular deep learning-based object detection frameworks, including RetinaNet 33
FCOS [59]. GFL [30], RCNN series [53] 4) YOLO series (52) 10,, and DETR [6], demonstrate
remarkable generalizability in the field of general object detection. Additionally, modern backbone networks such as ConvNext (42), VAN (22), LSKNet (31) and Swin Transformer [41] are designed
to efficiently and effectively model visual features. However, SAR image object detection poses unique challenges due to factors such as small object size, speckle noise, and sparse information inherent in SAR images. As a result, recent deep learning methods for SAR object detection primarily focus on network and module design to address these challenges. Approaches like MGCAN [9]. MSSDNet 91.. and SEFEPNet 79] enhance object features through multiscale feature fusion. Quad-FPN 82 combines four distinct feature pyramid networks for thorough multiscale feature interaction to alleviate noise interferences and multi-scale object feature misalignment. PADN 88 and EWFAN 65 employ attention mechanisms to enhance object features in the presence of SAR speckle noise. CenterNet++ (21) an extension of CenterNet [92], incorporates feature enhancement, multi-scale fusion, and head refinement modules to improve the detector's robustness specifically for SAR images. Additionally, CRTransSar 744. built on the nigh-performance Swin transformer 41] leverages context representation learning to enhance object features.
While most existing works concentrate on mitigating SAR speckle noise interference through network structure improvements, few attempts to address the issue at the level of input data. Furthermore, most studies utilize ImageNet pretrained backbones as the initialization overlooking the substantial domain gap between the pretrained nature scenes dataset and the finetuned SAR dataset, as well as the model gap between the backbone and the entire detection framework. Instead, we seek to address these unique challenges through a carefully designed pretraining strategy.
the detection framework,

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Table 1: Image and instance level statistics of MARDet-100K, dataset. *; Origin datasets are cropped into 512 x 512 patches. Ins: Instances, Img: Images.
Images
Instances
Dataset
Ins/img
Train
Val
Text
ALL
Train
Val
Test
ALL
AIR_ SARShip
76
438
23
40
501
816
33
209
1,058
2.11
AIR AIR_SARSEE SARShip
76
270
1.5
15
300
1,819
127
94
2,040
6.80
HRSID
X
3,642
981
981
5,604
11,047
2,975
2,947
16,969
3.03
MSAR*
75
27,159
1,479
1,520
30.158
58,988
3,091
3,125
65.202
2.16
SADD 30
795
44
44
883
6,891
448
496
7.835
8.87
SAR-AIRcraft
000
13,976
1,923
2,989
18,888
27, 27,848
4,631
5,996
38,475
2.04
ShipDalasel
07
31,784
3,973
3,972
39,729
40,761
5,080
5,044
50,885
1.28
SSDD X
928
116
1,160
2,041
252
294
2,587
2.23
OGSOD
SIVED 14,664
1,834
18.331
38,975
4,844
48.589
2.65
9,561
1,222
12.013
SARDet-100k
94,493
10,492
11,613
116.598
198,747
22,703
24,023
245.653
2.11
Table 2: SARDet- 100K source datasets information. GF-3: Gaofen-3, S-1: Sentinel-1. Target
categories S: ship, A: aircraft, C: car, B: bridge, H: harbour, T: tank.
Datasets
Target
Res. (m)
Band
Polarization
Satellites
License
AIR_ SARShip 76
1,3m
VV
GF-3
0.5m-3m
HV.
I.I,,TemmS TemaSAR-X.TaDDFEE. X.TaDDDMXX
General Public
MSAR 75
B, < Im
HH. HV. VH. VV
HISEA-1
CC BY-NC4.0
SADD 8D
0.5m-3m
IIII
TemaSAR-X
AAR-AIRerai
Im
Uni-polar
GF-3
CC BC
ShipDataset 67
3~-25m
HH. VV. VHL HV
S-1,GF-3
VV. VH, B.I.Rad TemmSAR-X
Apache2.0
OGSOD 6
B. H.
3m
VV/HH
GF-3
SIVED
35
C
0.1.0.3m
Ka,Ku,X
VIIIIII
Airhorne SAR synthetic slice
3
A New Benchmark Dataset for SAR Object Detection
3.1
Current Status
SAR images are typically captured by satellites, and there wealth of low-resolution SAR imagery available, often with Ground Sample Distance (GSD) of 10m x 10m or larger. Platforms like Sentinel-1 12 provide access these images, which offer a macroscopic view of various geophysical places such as cities, mountains, rivers, and cultivated land. This makes them particularly advantageous for scene classification tasks. However, the inherent low resolution of these images constrains their capability to delineate fine details of smaller objects, such as ships, cars, and airplanes. Conversely, high-resolution SAR images provide more detailed information but require significant hardware resources. Moreover, these high-definition images often encompass sensitive information, making them unsuitable for public release. Furthermore, acquiring high-resolution SAR datasets can be very expensive, posing significant challenges to their accessibility.
Numerous research teams frequently encounter budgetary limitations that restrict their capacity to obtain a large and diverse collection of high-resolution SAR datasets. These financial constraints not only limit the scope of geographical areas that can be covered but also affect the variety of data sources that can be accessed. Consequently, the datasets made available by these groups often lack diversity, particularly in aspects such as spectral bands, polarization, and resolution. From researcher's 's perspective, evaluating models on such small and homogeneous datasets can introduce bias and lead unfair performance comparisons.
3.2 SARDet-100K
To address the aforementioned challenges, we undertake thorough survey of SAR object detection datasets. As a result, we carefully collect a total of 10 publicly available high-quality datasets that are not only diverse but also have conflicting object categories. These data are released by or collected from different countries and institutions, such as scientific research departments in China, space departments in Europe, and military departments in the United States. Detailed information

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Huge Domain Gap
Pixel Space
Large Gap
Optical Bridge
Object
Bridge
WST Feature Space
Small Gap
Natural
Sensing
Sensing
Natural
Remote Sensing Datuset
RGB Dataset
RGB Dutaset
SAR Dataset
Dataset
(b)
Illustration of the significant domain gap exists between Nature RGB dataset and remote sensing SAR datasets. (a) showcases the WST feature space significantly narrows the domain gap. (b) demonstrates that the remote sensing RGB dataset serves as an effective domain transit bridge, facilitating smoother domain transfer.
Figure 2:
on the collected datasets is shown in Table. 2 To ensure consistency across the collected datasets, we invest considerable time and effort in rigorous dataset standardization. This involves addressing variations in train-val-test splitting status, image resolutions, and annotation formats. More details about data collection and standardization can be found in the Appendix.
Table presents the standardized sub sub-datasets of SARDet- 100K along with their corresponding statistics, which includes information on both image-level and instance-level statistics. The SARDet- 100K dataset encompasses total of 116,598 images, and 245,653 instances distributed across six categories: Aircraft, Ship, Car, Bridge, Tank, and Harbor. SARDet- 100K dataset stands as the first large-scale SAR object detection dataset, comparable in size to the widely used COCO R4 dataset (118K images), which the standard benchmark for general object detection. The scale and diversity of the AARDet-100F dataset effectively simulate real-world scenarios encountered in the application of SAR object detection models across multiple data sources. SARDet- 100K provides rescarchers with robust training and evaluation for advancing SAR object detection algorithms and techniques, fostering the development of SOTA models in this domain.
Multi-Stage with Filter Augmentation Pretraining Framework
have demonstrated the effectiveness of mature handcrafted features and specialized network module designs in improving SAR object detection performance. However, most of these works rely on the default ImageNet pretraining approach, thus overlooking the significant domain gap between the pretrained nature scenes dataset and the finetuned SAR dataset. Additionally, they fail address the model gap that exists between the backbone and the entire detection framework. To address these limitations, we propose a novel framework called the Multi-Staz Stage with Filter Augmentation (MSFA) Pretraining Framework. Our framework tackles the challenges from the perspective of data input, domain transition, and model migration. MSFA comprises two core designs: the Filter Augmented Input and the Multi-Stan Aulti-Stage pretrain strategy.
Several recent studies 26
78 74
4.1 Filter Augmented Input
As discussed in the Related Work section, numerous existing handerafted feature descriptors leverage meticulously designed filters to extract features. These features, robust and rich in information, act as augmented information derived from the original image. Thus, propose employing such features as auxiliary information alongside the original pixel data. The filter augmented feature M of data can be generally defined as:
5

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Traditional
MSFA
Filter Augmentation
BackDoan
Backhong
Pretrain
Stage
IN
ImageNNtt
Domain Gop
Model Gap
Backbone
Random
Stage
Model
DOTA
Gap
Backbone
Detection
Domain Bridge
Model
Finetune
Finetune
Model
SAR
Model Bridge
Figure 3: Conceptual illustration of traditional ImageNet pretrain and our proposed Multi-Stage with
Filter Augmentation (MSFA) pretrain framework.
M = i {HOG, Canny, Hoar, W ST, GRE}.
(I)
Where Т, is pre-defined transformation. Drawing inspiration from the information residual design in ResNet [23] we construct the Filter Augmented Input to the detection model, Tnp, by concatenating the original grayscale SAR image with the generated filter augmented feature M as:
Inp = concat(x, M,).
(2)
By casting the original data input from the heterogeneous pixel space homogeneous filter augmented feature space, the domain gaps between different image domains can be greatly reduced, illustrated in Fig. a).
4.1.1 Multi-stage pretrain
We formulate the traditional pretrain schema
B =
(3)
= TAa)n
(4)
The function Train, means training a model on dataset b with a task t, and it returns trained model. is the training task, {cls, det) where cls stands for classification and det for detection. B indicates the backbone model and A is the whole detection model. Traditionally. the pretrain stage will randomly initialize the backbone model Bg and train on the dataset ImageNet DIN (as Eq. (3). Then finetune on the SAR dataset DSAR with pretrained backbone initialised from detection model B (as Eq.
Our proposed multi-stage pretrain strategy, alternatively, can be illustrated as in the Eq.
A' Train (Ag)
(5)
A Train (Ax))
(6)
Where an extra second stage pretraining in Eq. (5) is added. We propose the utilization of large-scale optical remote sensing dataset, D DRs. as detection pretrain for domain transit. The dataset consists of optical modal imagery, which also shares similar object shapes, scales, and categories in downstream SAR datasets. This characteristic serves valuable bridge between the optical distribution of natural images in ImageNet and the object distribution in SAR remote sensing images. By leveraging such second stage pretrain, the domain gap effectively minimized, illustrated in Fig. 2b).

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4.1.2 MSFA
Finally, the proposed MSFA framework integrates Filter Augmented Input with Multi-Stage pretrain- ing, as illustrated in Fig. B] Our MSFA framework effectively bridges the substantial domain and modal gaps between pretramnnng on nature images and finetuning on SAR image detection.
By introducing Filter Augmented Input, leverage mature handcrafted feature descriptors to extract noise-robust features. This also enables us to effectively transform the heterogeneous image domains of both pretraining and finetuning images into homogeneous feature domain. By unifying the imput data into a consistent feature domain, we address the disparities that cxist between different types of images. Consequently, it enhances the alignment and transferability of knowledge across domains. Moreover, the incorporation of multi- training involves utilizing an additional large-scale optical remote sensing dataset for detection pretraining. This dataset acts domain bridge, connecting the domain of ImageNet's nature images with that of SAR remote sensing images. As a result, it further reduces the domain gaps, facilitating smoother transition between the two domains. Furthermore, the detection pretraining in the second stage of the MSFA framework can also act as model bridge. allows for the comprehensive training of the entire detection framework, rather than solely focusing on the backhone, making the whole detection framework well-initialized to perform optimally in the SAR detection finetuning.
Experiments and Analysis
5.1 Filter Augmented Input
To investigate and assess the impact of the proposed Filter Augmented Input, we conduct experiments each traditional feature descriptor discussed in the Related Work, within the
Input
mAP f
mAP5o
Domain
PCC 1
SAR (as RGB)
50.2
83.0
Pixel Space
0.394
SAR+Canny
50.7
83.6
Canny Space
0.992
SAR+Hog
50.7
83.5
Hog Space
0.995
SAR+Haar
50.6
83.4
Haar Space
0.990
SAR+WST
51.1
83.9
WST Space
0.996
SAR+GRE
50.6
83.8
SAR+Hog-Har--WST
51.1
84.0
GRE Space
0.984
Table 3: Comparison of different Filter Augmented Inputs using Faster R-CNN and ResNet50 as the detection model.
Table Pearson Correlation Coefficients (PCC) of ImageNet and SARDet-1000 on RGB and handcrafted feature spaces.
framework of our proposed MSFA method. The findings, detailed in Table 3 indicate that incorporat- ing these handcrafted features notably enhances the performance of the detector. Additionally, our analysis reveals that converting image pixels into handcrafted feature spaces significantly minimizes the distributional gaps between the ImageNet and the SARDet- 100K datasets. This is particularly evident in the Pearson Correlation Coefficients (PCC) between the inputs of the ImageNet and SARDet- Sinet-1000 100K datasets, illustrated in Table 4 This underscores the efficacy of the proposed approach in bridging the domain gap between natural and SAR images, thereby enhancing the efficiency of knowledge transfer from the pretraining process.
Remarkably, the Wavelet Scattering Transform (WST) feature stands out for its exceptional perfor- mance. This superiority can be attributed not just to its role in significantly narrowing the domain gap, but also to its capacity for extracting rich, multi-scale information. Such information acts as a robust auxiliary feature by mitigating noise and preserving object-related details. However, also find that using multiple filter augmented features will not have further significant performance gain. It is possible that the existing WST already captures the essential information necessary for effective object detection, and incorporating additional ones does not provide substantial additional beneficial information.
Due to the outstanding performance of WST, we employed as the default Filter Augmented Input in our MSFA method for the remainder of the paper.
7

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Table 5: Comparison of different pretrain strategies using Faster-RCNN and ResNet50 as the detection model.
ID
Model Input
mAP
Multi-stage
Component
X
ImugeNet
Backbone
49.0
2
SAR (Raw Pixels)
ImageNet DIOR
Frumework
49.5
Backbone
49.3
ImageNet DOTA
Framework
50.2
X
ImageNet
Backbone
49.2
SAR+WST
ImageNet + DIOR
Framework
50.1
7
(Filter Augmented)
Backbonc
49.6
ImageNet DOTA
Framework
51.1
5.2 Multi-stage Pretrain
To evaluate the effectiveness of the proposed multi-stage pretraining approach, we conduct experi- ments in which we keep the input modality consistent and finetuned the detection model using various pretraining strategies on the SARDet- 100K dataset. As baseline, Exp. takes single-channel SAR data as input, pretrains the backbone model ImageNet for 100 epochs, and then directly finetune the detector on the SARDet- 100 dataset (following the widely used default setting). In addition to optical remote sensing datasets, such as DOTA 7 or DIOR [27]. (Details on DOTA and DIOR datasets can be found in the Appendix). Following the second pretraining stage, finetune the model either solely on the backbone or on the entire framework.
the baseline,
perform second stage pretrain specifically for object detection
The results of Exp. 2, 4, 6, and 8 in Table prove the substantial advantages of the two-stage pretraining approach. Notably, even the relatively small-scale DIOR dataset showcases noticeable performance gains compared the baseline (Exp. 1 and 5). This observation underscores the significance of reducing the domain gap during the pretraining phase of SAR detection.
However, the DIOR dataset pretraining is not effective the larger-scale DOTA dataset (Exp. 2 vs 4, 8). This comparison underscores the significance of the pretraining scale achieving optimal results. The DOTA dataset, with its larger scale and similar average instance area to SARDet- 100K, provides a more comprehensive and informative pretraining, leading to improved performance during the subsequent finetuning stage.
The comparison between Exp. & 4 and Exp. 7 8, demonstrate the superiority of pretraining the entire framework over solely the backbone, highlighting the significant impact of the model gap on the performance of SAR detection.
In summary, our proposed multi-stage pretraining strategy in MSFA alleviates both the data domain gap and the model gap between pretraining and the downstream model, leading to significant enhancements in SAR detection performance. Detailed experimental results and visualization are available in the Appendix.
5.3 Generalizability of MSFA
To assess the effectiveness and generalizability of the proposed MSFA, we conduct experiments using various detectors and backbones, as presented in Fig.
Fig. 4a) ab) Significant performance
improvement is observed across different frameworks (including single-stage [53 43 two-
stage 33 30 59], and end2end (94 58 391) and diverse backbones (including ResNets (23],
4l networks). It provides strong evidence for the effectiveness and wide applicability of our proposed method. Furthermore, we observe stable performance improvements
ConvNexts 42), VANs 22] and Swin-Transfonee
scale up the backbone size, shown in Fig. b). indicating the good scalability of our proposed method.
Significantly, the design of our MSFA method was developed with flexibility, generalizability and wide applicability in mind. Therefore the method can seamlessly integrated into most existing models without any modifications.
8

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Faster RCNN
Spanie RCNN
Cascade RCNN
f1.4
45.0
53.9
SARDet- 100K MAP (9)
54
Two-Stage
Dab-DETR
46.2
45.9
48.0
Grid
52
Single Single-Stag Stagl
Stage
50
Deformable
DETR
$1.3
50.0
46.5
49.8
47.4
19.0
RelinaNet
48
Reseet
Camneett
MSRA
VAN
53.7
46
Swin
GFL
20
40
60
80
MSFA INN
Param, (M)
(a)
(b)
Figure 4: Generalization of MSFA on different detection frameworks (a) and different backbones (b). Models are finetuned and tested SARDet- 100K dataset. INP: Traditional ImageNet Pretrain backbone network only.
Table 6: Comparison of the proposed MSFA with previous state-of-the-art methods on SSDD and HRSID datasets.
Open Sourze
mAP 5c f
Detectors
Year
SSDD
IRRIID
Grid R-CNN 43
2019
88.9
79.4
Faster R-CNN
5
2015
89.7
Cascade R-CNN
2019
90.5
General
Free-r
Detectors
Double-Head R-CNN
72
2020
91.1
82.1
PANET 4D
2018
91.2
81.6
DCN 14
2017
92.3
82.1
NNAM 7
X
2019
79.8
DCMSNM A5
2018
89.6
ARPN 87
2020
89.9
$1.8
DAPN 13
2019
90.6
81.8
HR-SDNet
2020
FBR-Net
NRENet
2024
94.6
75.6
CenterNet++
21
X
2021
95.1
SRrransSar
X
2022
97.0
SARATR-X 1N
X
2024
97.3
80.3
Faster R-CNN VAN-B
2023
92.9
$1.8
MSFA (Faster R-CNN VAN-B)
2024
97.9(45.0)
83.7(-1.9)
5.4 Comparison with SOTAS
We compare various SOTA methods, including both general object detection models 4 533
64
87 [2]. as well as SAR object detection models 25 89 13 70 40 37 74 20 We evaluate their performance on the SSDD and HRSID datasets, which are the commonly used benchmarks for SAR object detection. To leverage the superior efficiency and performance of the VAN (22] backbone (as shown in Fig. 4fb)), we employ the classic faster R-CNN detection framework with the lightweight VAN-B (Param. 26.6M) backbone our detection model. The results presented in demmonsddtostttraee that our MSFA method outperforms all the compared methods by significant margin. Specifically, MSFA achieves mAP@50 50 of 97.9% on the SSDD dataset and mAP@ of 83.7% on the HRSID dataset, setting new state-of-the-an results. It is noteworthy that our method is the only open- sourced method among the compared SAR detection SOTAs.

---

Limitaion and Future Work
The scope of this paper is limited to supervised pretraining. However, considering the availability of a vast amount of unannotated SAR images, it would be valuable explore the potential of semi-supervised, weakly-supervine or unsupervised learning methods for domain transfer in SAR object detection.
While this paper aims to propose a simple, practical, effective, and generic method, does not delve into the details of specific designs. Future work can be expanded to explore the aforementioned directions in more depth, incorporating intricate and specialized designs to enhance the performance and capabilities of SAR object detection.
Conclusion
This paper presents new benchmark for large-scale SAR object detection, introducing the SARDet- 100k dataset and the Multi-Stage with Filter Augmentation (MSFA) pretrain method. Our SARDet- 100k dataset comprises over 116K images spanning 6 categories, providing large and diverse dataset for conducting SAR object detection research. To bridge the domain and model gaps between pretraining and finetuning stages in SAR object detection. we propose the MSFA pretraining framework. MSFA significantly enhances the performance of SAR object detection models, setting new state-of-the-art performance previous benchmark datasets. Moreover, MSFA demonstrates remarkable generalizability and flexibility across various models. Our research endeavours to overcome the current obstacles prevalent in SAR object detection. We anticipate our contributions will pave the way for future research and innovations in this domain.
Our research endeavours overcome the current obstacles prevalent in SAR object detection. We anticipate our contributions will pave the way for future research and innovations in this domain.
8 Acknowledgement
We extend our deepest gratitude to the following researchers, listed alphabetically by first name: Hong Zhang, Runfan Xia, Shunjun Wei, Tianwen Zhang, Xian Sun, Xiaofang Zhu, Xiaoling Zhang, and other contributing researchers, for allowing us to integrate their datasets. Their contributions have greatly advanced and promoted research in this field.
This work is supported by the National Science Fund of China (62361 166666,,6276/1111 62176130, 62206134) and the Fundamental Research Funds for the Central Universities (Nankai University: 070-63233084, 070-63243142). Computation is supported by the Supercomputing Center of Nankai University (NKSC).
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A
Appendix
A.1
SARDet-100K Dataset Visualization
Fig. S5 offers a visualization of sample images from the proposed SARDet-100K dataset. Repre-
sentative samples for each category, including Ship, Tank, Bridge, Harbour, Aircraft, and Car, showcased.
Ship
Tank
Bridge
Harbour
Aircraft
Car
Figure S5; Visualization of sample images from the proposed SARDet-100K dataset.
A.2 Standardization.
We collect total of 10 publicly available tigh-quality datasets that not only diverse but also have no conflicting object categories. These data are released by or collected from different countries and institutions, such scientific research departments in China, space departments in Europe, and military departments the United States. To ensure consistency across the collected datasets, is necessary undertake standardization processes. This involves addressing variations in train-val-test splitting status, image resolutions, and annotation formats. The overview of SARDet- 100K dataset process pipeline is illustrated in Fig. a)
the source dataset already provides predefined train, validation, and test splits, we adopt their split settings. Otherwise, perform the splitting by ensuring ratio of 8:1:1 for the train, validation. and test sets respectively.
Additionally, we tackle the issue of high image resolution in some of the collected datasets. This concern arises because resizing these images before passing them to the model could result in extremely small targets. We perform image slicing all datasets that contain images larger than
16

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Raw Datasets
1. Set Split
Train Set
Harhor 2%
Tank 11%
5%
Category Instunce Proportion
Ship 47%
Val Set
Bridge 14%
Test Set
Train
Train Set
21%
5490
1791
1781
461
Val Set
Test Set
Val Set
Test Set
Harbor
Aireraft
Car Bridge Ship Tank
Label Unification
Image Slicing
Relative Average Instance Area
(a)
(b)
Figure S6: (a) SARDet- 100K dataset standardization process, encompassing set splitting, large image slicing, and label annotation format unification. (b) Percentage of instances for each category and average instance area (in pixels) in SARDet-100K.
1000 1000 resolution. Specifically, for AIR SARShip 1, MSAR, and SAR-AIReraft datasets, crop each image into patches of size 512 x 512 with a patch overlap of 200.
Furthermore, convert all dataset annotations into the COCO annotation format (34] This step ensures consistency and compatibility among the different datasets. Consequently, the merged dataset, SARDet-1008 is also standardized in the COCO format, which is readily compatible with popular open-source detection code frameworks, eliminating the need for additional manual data preprocessing. Fig. S6 b) provides overview of the category-leve statisties for the MARDet-1008 dataset.
17

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A.3 Handcrafted Feature Descriptors
Histogram of Oriented Gradients 15] (HOG). HOG is a widely used local feature descriptor in image processing and computer vision. It captures and represents the image's local structure and shape information by analyzing the distribution of gradient orientations. HOG is proven be effective for SAR image classification and object detection tasks because it is invariant to random noise in SAR images. Early works employ HOG for SAR object recognition [66] 49 and recent
studies demonstrate the effectiveness of HOG features for SAR image classification as the model pretraining (66] in modern neural networks.
83 as well
Canny Edge Detector [5]. Canny is a widely used edge detection algorithm, which aims to identify significant edges in image while minimizing noise and spurious responses. The algorithm utilizes Gaussian smoothing, pixel orientation gradient magnitude, and non-maximum suppression. Early research [28) 38 recognize the advantage of Canny for SAR image processing. Recent works also verify the effectiveness of the Canny feature in SAR image edge detection [69]. interference detection [18), and image registration [86). Other than Canny Edge Detector, Gradient by Ratio Edge
(GRE) [29], a recently proposed edge detector, leverages SAR-HOG [1 and SAR-SIFT [56] to achieve effective edge detection SAR images.
Haar-like 62 Feature Descriptor. Haar-like features are commonly used for face detection 46 2
pedestrian detection [8], and other target detection tasks [32) 36). They describe the characteristics of an image by utilizing predefined feature templates. Haar-like features can capture linear features, edge features, point features, and diagonal features. Early studies [54 555 demonstrate the robustness of Haar-like features for SAR object detection. A recent approach called MSRIHI. [ integrates low-level Haar-like features into deep learning models for accurate SAR object detection, highlighting its potential effectiveness.
technique that widely used in image processing. aims to extract robust and discriminative low-level and high- level features simultaneously. By capturing both high-frequency and low-frequency information, provides a rich representation of both local and global image features. The hierarchical representation offered by the Wavelet Scattering Transform enables feature extraction different scales and resolutions, which is particularly useful for capturing fine details of small objects in SAR images and robustly handling high-frequency noise. Vidal et al. [6 conduct comprehensive investigation demonstrating the high potential performance of Wavelet Transform in SAR image denoising. Many recent works [93 26 50 78 utilize Wavelet Transform or WST for robust feature extraction and integration with CNN networks for target recognition. These studies validate the feasibility of incorporating WST features into modern CNN models.
Wavelet Scattering Transform 45 (WST). The WST is powerful
SAR
HOG
Canny
GRE
Haar
WST
image
image
Figure S7:
Visualization of handcrafted features on SAR images. (To facilitate visualization, the features are average pooled and represented as single channel.
A.4 Multi-Stage Filter-Augmentationn
Fig. S8 provides a visual illustration of the proposed filter-aummented data input. It involves concate- nating original single-channel grayscale Synthetic Aperture Radar (SAR) image, denoted as x, with the filter-augmentation representation M.
18

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Detection Model
SAR
X
Inp
Detection Model
SAR +
T
Filter Augmentation
Figure S8: Illustration of filter-augmened input.
Table S7: Comparsion of different filter augmented inputs using Faster R-CNN and Resnet-50 as detection model.
Modality
mAP
@50
@75
@
@
@1
SAR RGB)
50.2
83.0
54.8
44.8
61.6
58.6
SAR+Canny
50.7
83.6
55.0
45.3
62.0
57.1
SAR+Hog
50.7
83.5
55.2
45.1
61.4
58.4
SAR+Haar
50.6
83.4
54.7
45.4
61.6
58.0
SAR+WST
51.1
83.9
54.7
45.2
62.3
57.5
SAR+GRE
50.6
83,8
54.7
44.8
61.7
57.6
SAR+Hog+Haar+WS)
51.1
84.0
55.9
45.7
62.0
58.2
Table $8: Generalization of MSFA different detection frameworks. INP: Traditional ImageNet Pretrain on backbone network only.
Test
Framework
Pretrain
@50
@75
m
INP
49.0
82.2
52.9
43.5
60.6
55.0
Faster RCNN
MSFA
51.1 (621)
83.9
54.7
45.2
57.5
Two
INP
51.1
81.9
55.8
44.9
62.9
60.3
Cascade RCNN
Stage
MSFA
53.9 (+28)
83.4
59.8
47.2
66.1
63.2
INP
48.8
79.1
52.9
42.4
61.9
55.5
Grid RCNN
MSFA
51.5 (+2.)
81.7
56.3
45.1
64.1
60.0
INP
47.4
79.3
49.7
40.0
59.2
57.5
RetinaNet
33
MSFA
(9.0
80.1
52.6
41.3
61.1
59.4
Single
INP
49.8
80.9
53.3
42.3
62.4
58.1
GFL
Stage
MSFA
53.7 (43.9)
84.2
57.8
47.8
66.2
59.5
INP
46.5
80.9
49.0
41.1
59.2
50.4
FCOS 39
MSFA
48.5 (+2.0)
82.1
51.4
42.9
60.4
56.0
INP
31.8
62.3
30.0
22.2
44,9
41.1
DETR
MSFA
(+154))
77.5
49.8
37.9
62.9
58.2
End to
INP
50.0
85.1
51.7
44.0
65.1
61.2
Deformable DETR
End
MSFA
51.3
85.33
54.0
44.9
65.6
61.7
INP
38.1
68.8
38.8
29.0
51.3
48.7
Sparse RCNN
MSFA
41.4
74.1
41.8
33.6
53.9
53.4
INP
45,9
79.0
47.9
38.0
55.0
Dab-DETR
39
MSFA
48.2 (+2.3)
81.1
$1.0
41.2
63.1
55.4
19

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Table S9: Generalization of MSFA on different detection backbones. INP: Traditional ImageNet Pretrain on backbone network only.
Test
Framework
#P(M)
Pretrain
mAP
@50
@75
@s
m
INP
49.0
82.2
$2.9
43.5
60.6
55.0
R50
23
25.6
MSFA
51.1 (42.1)
83.9
54.7
45.2
62.3
57.5
INP
51.2
84.1
61.9
56.3
R101
MSFA
52.0
84.6
56.6
46.6
63.4
57.7
INP
51.9
85.2
46.4
62.5
57.9
R152
60.2
MSFA
52.4
85.4
57.2
47.4
63.3
58.7
INP
53.2
86.3
58.1
47.2
65.2
59.6
ConvNext-T
28.6
MSFA
54.8 (+1.6)
87.1
59.8
48.8
66.7
62.1
INP
54.2
87.8
59.2
49.2
65.8
59.8
ConvNext-S 42
50.1
MSFA
55.4
87.6
60.7
50.1
67.1
61.3
INP
55.1
87.8
59.5
48.9
66.9
61.1
ConvNext-BB B 42
88.6
MSFA
56.4 (+1.3)
88.2
62.4
INP
45.8
79.8
48.0
38.6
57.9
53.3
VAN-T 22
4.1
MSFA
47.6 (+1.8)
81.4
50.6
40.5
59.4
56.7
INP
49.5
83.8
52.8
43.2
61.6
56.4
VAN-S 22
13.9
MSFA
51.5 (+2.0)
85.0
55.6
44.8
63.4
60.4
INP
53.5
86.8
58.0
47.3
65.5
60.6
VAN-B
26.6
MSFA
55.1 (+1.6)
87.7
60.2
48.8
67.3
62.2
INP
48.4
83.5
50.8
42.8
Swin-T
28.3
MSFA
50.2
84.1
53.9
44.1
61.3
58.8
INP
53.1
87.3
57.8
47.4
63.9
60.6
Swin-S
49.6
MSFA
(4.0
87.0
59.2
48.2
64.5
61.9
INP
53.8
87.8
59.0
49.1
64.6
60.0
Swin-B 41
87.8
MSFA
55.7
87.8
61.4
50.5
66.5
62.5
A.5
Experimental Results
A.5.1
Main results on SARDet-100K.
The detailed experimental outcomes are presented in Table S7 Table S8 and Table S9
where com-
prehensive, fine-griined testing metrics provided. These include AP@50. AP@7 @77. AP small (AP@s), AP@medium (AP@m), and AP@ large (AP@I), offering deeper insights and robust results for model evaluation.
Table S7 serves an extension of Table 3 from the main paper, showcasing comparison of various filter iiter-augmented inputs utilizing Faster R-CNN with ResNet-50 as the detection model.
Table S3 expands upon Figure 6(a) of the main paper, and Table SY expands upon Figure 6(b). These tables centre on the experiments that explore the generalizability of the MSFA framework across different detection frameworks and backbones. Notably, a significant enhancement performance is observed across various frameworks, including single-stage, two-stage, and query- query-based frameworks, as well across a spectrum of backbone architectures, ranging from ResNets and modern-designed ConvNets to Vision Attention Networks (VANs) and Vision Transformer (ViT)-based Swin networks. These results offer compelling evidence of the efficacy and broad applicability of the method we propose.
20

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Table S10: Dataset statistics comparison between DOTA and DIOR. multi-scale preprocessed.
Dataset
Images
Instances
Categories
Image Size
Avg. Instance Area
DOTA*
68,324
1,058,641
15
1024*1024
5,021
DIOR
23,463
192,518
20
800*800
12,726
Table S11: Comparison of MSFA and Finetuning Performance under similar computational budgets. INP: Backbones are pretrained on ImageNet.
Model
INP
MSFA Epoch
Finetune Epochs
Total Epochs
Total Iterations
mAP
Faster-RCNN
24
16.1k
54.5
Paster-RCNN
36
17.7k
52.8
To ensure a fair comparison. we evaluate MSFA and vanilla detection models under similar com- putational budgets. In Table we train Faster-RCNN models (with a ResNer-50 ImageNet- 1 pretrained backbone) with 12 epochs of MSFA pretraining on the DOTA dataset, followed by finetun- ing on the -ARDet-110K dataset. This is compared to directly finetuning the model for 36 epochs on the SARDet- 100K dataset. With the same total number of training epochs and slightly less total number of iterations, the proposed MSFA achieves significantly higher (1.7% higher) mAP result on SARDet- 100K test set. MSFA pretraining incorporates general knowledge from both natural and remote sensing datasets, which allows efficient and effective finetuning and helps mitigate overfitting in downstream tasks. It is also important to note that, MSFA pretraining is a one-time effort. The pretrained MSFA models can be reused for finetuning different SAR detection datasets.
A.5.2 Comparison of SARDet-100K with other datasets.
To assess the quality the proposed SARDet-100K dataset as a large-scale SAR object detection benchmark, we evaluate diffferent models on this dataset and compare the results with those obtained from other popular benchmark datasets, such as SSDD 84) and HRSID (71.. The results are dataset has not yet reached saturation. Among the various models reviewed, there is an 8.4% performance gap between the weakest and strongest models, whereas, for SSDD and HRSID, this gap is only 4.1 % and 4.3%, respectively. This suggests that SSDD and HRSID are relatively simple for most existing models, leading to performance saturation on these smaller datasets.
presented in Fig S9 These results indicate that the performance of modern models
Furthermore, we observed that on these smaller datasets, models with a relatively large number of parameters tend to suffer from performance degradation due to overfitting. For example, ResNet-152 underperforms compared to ResNet-101 on SSDD, and ResNet- 101 underperforms compared to ResNet-50 and ResNet-18 on HRSID. However, the large-scale SARDet- this issue does not arise. On our larger dataset, models continue to benefit from increased model size, indicating that proposed dataset is suitable for developing relatively larger models for large-scale SAR object detection.
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95.0
92.5
Detection mAP@50
90.0
87.5
85.0
82.5
80.0
Resnel
SARDet-100K
VAN
SSDD
77.5
Swin
75.0
20
40
60
80
Param. (M)
Figure S9: Evaluate different backbone models on SARDet-100K dataset and other previous popular benchmarks (SSDD (84) and HRSID (7)). Backbones are plugged under Faster-RCNN [53 detection
framework.
A.6 Implementaion Details
For the ImageNet pretrain, we adopt a 100-epoch backbone pretraining strategy on the Imagenet-IK with their default training strategy in MMPretrain [11] configurations.
For the second stage of the proposed multistage pretrain, we select large-scale optical remote sensing DOTA dataset as the main dataset. We also do comparison experiments on the DIOR dataset to find out the downstream performance affection factors on the second stage pretrain dataset. Their detailed information is illustrated in Table
For the DOTA dataset, we perform an extra dataset preprocess because DOTA dataset images have large range of different image resolutions. The original image contains only 1,411 training images. To have more image and multiscale instances for effective training, we follow BT adopt a multi-scale dataset splitting strategy by rescaling the original high-resolution images into three distinct scales (x0.5, xl.0, x1.5), and then cropping each scaled image into 1024x1022 patches with an overlap of each patch of 500 pixels, to avoid destruction division the instance at patch borders. With the preprocessed dataset, load resize as 1024* 1024, with a RandomFlip probability of 0.5. For the DIOR dataset, train the model by resizing the images 800, with RandomFlip probability of 0.5.
For finetuming on HARDet-100k, SSDD and HRSID, we train by resizing the image into 800*800, with RandomFlip probability of 0.5. We train the model by 12 epochs on the train set and test the model on the test set with the 12th epoch checkpoint.
We primarily conduct our experiments using the MMPretrain 11 and the MMDetection g frame- works, 8 RTX-3090 GPUs (24G). For detailed information on the hyperparameters and training settings, please refer to Table S12
A.7 Detection Result Visualizations
The detection visualization results, comparing the MSFA framework with the traditional ImageNet pretraining approach, are presented in Fig. [TO0 These results demonstrate that MSFA outperforms the conventional ImageNet backbone pretrain in terms of reducing missed detections, false detection, and improving localization accuracy.
22

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Table S12: Hyper-parameter of pretrain and finetune settings. Cls.: Classification, Det.: Detection,
B.S.: Batch Size, L.R.: Learning Rate.
Task / Model
Dataset
Optim.
B.S.
L.R
Epochs
Cls. Pretrain
ImageNet
AdamW
512
Ie-8
100
Det. Pretrain
DOTA
AdamW
le-4
12
Det. Pretrain
DIOR
AdamW W
Ic-4
12
Det. Finetune
SARDet-100k
AdamW
16
lc-4
12
Det. Finetune
SSDD
AdamW
32
2.5e-4
12
Det. Finetune
HRSID
AdamW
32
2.5e-4
12
DETR
DOTWSARDee-100K
AdamW
le-4
150
Deformable- DETTR
DOTA/SARDet- 100k
AdamW
2e-4
50
Dab-DETR
DOTA/SARIDet 100k
AdamW
16
Ic-4
50
Sparse-RCNN
DOTA/SARDet-100K
AdamW
16
2.5e-5
12
Table S13:
ConvNext-B MSFA
Category
mAP
@50
@75
@ m
ship
0.669
0.923
0.783
0.653
0.714
0.555
aircraft
0.455
0.753
0.466
0.419
0.458
0.47
car
0.655
0.985
0.792
0.553
0.674
0.454
0.766
0,427
0.429
0.891
bridge
0,441
0.885
0.368
0.411
0.609
0.714
harbor 0.711
0.979
0,858
0.601
0.751
0.756
Average
0.564
0.882
0.616
0.511
0.733
0.624
A.8 Failure Scenarios
However, the current model is not without its drawbacks and imperfections. Fig. STT highhightt several failure scenarios. When the input SAR images lack identifiable details contextual information, it can lead to incorrect classifications. When SAR images contain small and densely packed objects, the model may fail to detect some of them. Poor image quality characterized by fading, blurriness, overall low resolution, further exacerbates the risk of missed detections. In challenging cases, the detector may also struggle with accurate localization.
Fig.
presents the detection results of Faster-RCNN [33 with ConvNext-B [42] and MSFA pretraining. Notably, the model exhibits relatively low performance for objects with:
Regarding fine-gran fine-grained category detection performance, Table S13
Small sizes, such as Tank (with average area of 461 pixels)
Large length-width ratios, such as Bridge
High appearance variability, such as Airplane
However, the primary focus of this work is address the domain and model gaps existing in SAR object detection pretraining and finetuning, Our method demonstrates excellent compatibility with most existing deep networks and can seamlessly integrate with models specifically designed tackle the above challenging scenarios.
23

---

tank tank tankc
GT
Ca
INP
MSFA
Tank: 33.43
(a) Missing detection
bridge
GT
INP
MSFA
(b) False detection
GT
bri
INP
Orage:
MSFA
(c) Inaccurate localization
Figure S10: MSFA better than traditional ImageNet backbone pretrain in (a) missing detection, (b)
false detection and (c) inaccurate localization
24

---

GT
MSFA
False Classification
Miss Detection
Inaccurate Localization
Figure S11: Visulization of failure cases: false classification, miss detection, inaccurate localization.
25

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Reviews:
[{"review_id": "wynOsuqgpq", "reviewer": "Reviewer_qrry", "summary": "This work introduces SARDet-100K, a new large-scale, multi-category dataset for SAR object detection. It also proposes a novel Multi-Stage with Filter Augmentation pretraining framework to mitigate domain and model gaps encountered when transferring models pretrained on RGB datasets to SAR datasets. A new benchmark dataset and open-source method in SAR object detection is established.", "questions": "None", "limitations": "Limitations are well discussed.", "rating": 7, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 4, "strengths": "The paper's most significant contribution is the creation of the SARDet-100K dataset, which is the first large-scale, multi-category benchmark for SAR object detection. This dataset addresses the long-standing issue of limited and homogeneous SAR datasets, providing a rich resource that is likely to stimulate further research and development. The introduction of the MSFA pretraining framework is another strength, as it effectively addresses the domain and model gaps between RGB and SAR imagery, demonstrating robust performance across various deep learning models. \nAdditionally, the paper is well-written, clearly outlining the motivation, methodology, and implications of the research, making it accessible to a broad audience in the field.", "weaknesses": "The concept of the 'model gap' is not clearly defined in the current manuscript. Could you provide a more detailed explanation of what you mean by 'model gap' within the context of your study?\n\nInstead of a time-consuming pretraining stage, why not directly train the SAR detection model using only the proposed dataset with sufficiently long iterations since the dataset already contains a sufficient amount of images and instances.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "In the context of our study, the term 'model gap' refers to the inconsistency introduced when only the backbone of the detector is pre-trained, while other components, such as the neck and heads, are initialized randomly. For downstream object detection tasks, the model comprises the entire detector, which includes the backbone, neck, and heads. When performing object detection, initializing only the backbone while randomly initializing the other components creates a mismatch or gap between the pre-trained weights and the fine-tuned detector network. This inconsistency, referred to as the 'model gap,' can negatively impact the overall performance of the detector as the synergy between the backbone and other components is disrupted.\n\nWe have considered the approach of directly training the SAR detection model on the proposed dataset. However, there are two main reasons for opting for the pretraining method:\n\n1. Versatility of Pretrained Models: Our proposed method involves pretraining the model once, which can then be utilized for object detection across multiple SAR datasets. For instance, in our study, we pre-trained the model using the MSFA framework and demonstrated its applicability on the SARDet-100K, SSDD, and HRSID datasets. This 'one-for-all' pretraining approach saves considerable time during the fine-tuning stage, as the pretrained model serves as a robust starting point for various datasets.\n\n2. Mitigation of Overfitting: Pretraining helps reduce the risk of overfitting. As illustrated in Table S11 of our manuscript, pretraining on the DOTA dataset for 12 epochs followed by fine-tuning for 24 epochs (totaling 16.1K iterations) resulted in an mAP of 54.5. In contrast, directly fine-tuning for 17.7K iterations without pretraining yielded a lower mAP of 52.8. This demonstrates that the pretraining stage not only enhances performance but also provides a more efficient training process.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "CC5f6oAkOw", "reviewer": "Reviewer_paPp", "summary": "This study presents the a large-scale dataset designed for SAR object detection, alongside a Multi-Stage with Filter Augmentation pretraining framework. The authors address the challenges associated with the limited availability of public SAR datasets and the lack of accessible source codes. The proposed method is effective and can be generalized to most modern backbone and detection networks.", "questions": "In Figure 1, are the compared image pairs spatially aligned? It seems not. Would it be an issue?", "limitations": "None.", "rating": 7, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The establishment of the SARDet-100K dataset provides a robust foundation for large-scale, multi-class SAR object detection research. Moreover, the open sourcing of the SAR detection codebase significantly enhances research reproducibility. The introduction of the MSFA pretraining framework is a novel approach that effectively bridges the domain and model gaps between RGB and SAR datasets by leveraging traditional handcrafted features. Overall, the paper is well-written, and the experimental validation and analysis are sound and solid.", "weaknesses": "Your novelty lies in incorporating handcrafted features, the introduction and related work on these features are somewhat lacking. Given the current dominance of deep learning methods, many junior researchers may not be familiar with classic handcrafted feature descriptors. Therefore, it would be beneficial to provide a more comprehensive introduction and conceptual visualization for each of the mentioned handcrafted features to enhance the paper's clarity and accessibility. \n\nThe supplementary materials would benefit from including a clear, step-by-step guide on training and testing the models. While the code is provided, the lack of direct scripts and instructions makes it difficult to replicate the results.\n\nThe category distribution in Figure S6(b) reveals SARDet-100K is a significantly imbalanced dataset. This imbalance may lead to the long-tail problem, potentially hindering the performance of models on tail categories. Why do not you consider balancing the dataset?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We appreciate your feedback regarding the introduction and related work on handcrafted feature descriptors. We have indeed provided more extensive related work on these descriptors in the Appendix section of our submission. In the revised version of the paper, we will include comprehensive visualizations of different handcrafted feature descriptors (including HOG, Canny, GRE, Haar and WST) to enhance clarity and accessibility, as Figure R1 (in the rebuttal pdf).\n\nThank you for pointing out the need for a clearer guide in the supplementary materials. Our code is based on the open-source mmdetection framework, and a detailed guide can be found in the official mmdetection GitHub repository. However, we acknowledge that a more specific guide related to our work would be beneficial. Therefore, we will include a clear, step-by-step guide on training and testing the models directly in our open-sourced code to facilitate easier replication of our results.\n\nThe category distribution in Figure S6(b) indeed reveals an imbalance in the SARDet-100K dataset. We intentionally maintained this long-tail distribution to simulate real-world applications of large-scale multi-category SAR object detection, where such imbalances commonly exist. Addressing the long-tail problem is a significant challenge and an active area of research. Our work aims to provide a realistic benchmark for future studies that may focus on developing techniques to address these imbalances effectively.\n\nYes, the image pairs in Figure 1 are not spatially aligned, except for the second column. This misalignment is due to the difficulty in finding publicly available SAR-RGB paired images. The purpose of Figure 1 is to provide a conceptual overview, and the lack of spatial alignment does not hinder the understanding of the key concepts being presented.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "wcFxcXP8N9", "reviewer": "Reviewer_qv3Q", "summary": "The authors establish a new benchmark SAR object detection dataset (SARDet-100K) and open-source SAR detection pretrain method (MSFA). This initiative significantly addresses the limitations posed by the scarcity of public SAR datasets and the inaccessibility of source codes, fostering further research and development in SAR object detection.", "questions": "Regarding the image slicing in your dataset standardization process, how do you handle objects that lie on the slicing border? Will the slicing split the objects apart?", "limitations": "Limitations are well discussed in Section 6 and A.8.", "rating": 7, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "Providing a larger standardized dataset for the data-scarce field of SAR target detection addresses a critical need and significantly contributes to its development. The authors unify and standardizes ten existing datasets to create SARDet-100K, the first COCO-level large-scale dataset for SAR multi-category object detection, which represents a substantial effort.\n\nMSFA model proposed in this paper is both effective and concise. It is refreshing that the MSFA model ingeniously applies traditional handcrafted features instead of design-heavy deep learning methods. Moreover, unlike previous approaches that use handcrafted features for feature refinement in deep learning, this work employs them for model pre-training and domain transformation, representing a novel and innovative approach.", "weaknesses": "The paper does not clarify if the metrics reported on the SARDet-100K dataset are for the test set or the validation set. Additionally, please clarify the training setting: is the model checkpoint used for testing from the best validation or from the last epoch?\n\nAs a benchmark dataset and method, actual runtimes and memory usage should be reported. These details are currently missing from the paper.\n\nThere is a lack of clarification on the abbreviations used in Table 1 and S12.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Sorry for the lack of clarity in the manuscript. The reported performance metrics are based on the test set of the SARDet-100K dataset. The models are trained on the training set for a total of 12 epochs, and the checkpoint from the 12th epoch are used for testing.\n\nWe acknowledge the importance of reporting actual runtimes and memory usage. In response, we will release all training checkpoints and logs as part of our open-source code repository. These logs will include detailed information such as actual runtimes, memory usage, hardware specifications, system environment, package versions, and training loss, ensuring full transparency and reproducibility.\n\nSorry for any confusion caused by the abbreviations used in Table 1 and S12. Here are the clarifications:\nIns.: Instances Img.: Images Cls.: Classification Det.: Detection B.S.: Batch Size L.R.: Learning Rate.\n\nThank you for pointing out this practical issue. In our dataset standardization process, we handle objects that lie on the slicing border as follows: If at least 50% of the object’s area remains within the image slice, we keep the bounding box; otherwise, we ignore the bounding box and treat it as background. For the kept bounding boxes, we retain the original full box coordinates, meaning that the annotation coordinates for such objects may extend beyond the image boundaries. \n\nWe will add all the above details and clarifications to the revised version of the paper.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "K2KAgtWehl", "reviewer": "Reviewer_1iNV", "summary": "The authors address the limitations of existing datasets and the inaccessibility of source codes by creating a new benchmark dataset, SARDet-100K, which is a large-scale, multi-class dataset. Additionally, the paper proposes a Multi-Stage with Filter Augmentation pretraining framework designed to overcome the domain and model gaps between pretraining on RGB datasets and finetuning on SAR datasets. The MSFA method demonstrates effectiveness and generalizability across various models.", "questions": "See above.", "limitations": "Limitations are properly addressed.", "rating": 8, "confidence": 5, "soundness": 4, "presentation": 4, "contribution": 4, "strengths": "1. The creation of the SARDet-100K dataset is a significant contribution. It offers the research community a large-scale, diverse dataset that was previously lacking.\n2. As I know, in the field of SAR object detection, open-source code is indeed rare, which has significantly hindered the progress of development. It is very exciting to see a well-documented and professional open-source code base. Making the dataset and code publicly available enhances the reproducibility of the research and facilitates further innovation by other researchers.\n3. Most previous work on improving SAR object detection performance focus on designing neural network modules. It is interesting that this research tackles the problem from the perspective of pretraining and domain transition.\n4. The paper provides sufficient and detailed experiments/analysis that validate the effectiveness of the proposed MSFA method.", "weaknesses": "1. You mentioned that optical remote sensing datasets (like DOTA) shares similar object shapes, scales, and categories in SAR datasets, therefore the downstream SAR datasets detection can benefits from the transferred knowledge. But why not discuss joint train the DOTA and SARDet-100K datasets together? In this way, the model can also learn joint representation from both DOTA and SARDet-100K therefore potentially improves the detection performance on SARDet-100K.\n2. It is recommended to add a few more recent methods for comparison in Table 5.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We appreciate the reviewer's insightful comments and suggestions. We have carefully considered the points raised and provide the following clarifications and additions:\n\nWhile it is true that merging multiple datasets can improve performance in **general object detection** tasks where datasets share similar concepts (e.g., optical concepts), this approach has been shown to improve performance due to shared features and representations. However, in our scenario, SAR and optical (RGB) datasets represent different modalities, which present distinct challenges:\n\nSAR and RGB datasets differ significantly in terms of data modality and conceptual representation. Joint training of these datasets can result in a crowded feature and representation space, limiting the model's expressiveness for SAR data.\n\nThe distinct nature of SAR and RGB data leads to different learning difficulties, causing unsynchronized optimization rates across various parts of the model. Inconsistent optimization objectives across modalities can cause conflicting optimization directions, further reducing the performance of SAR object detection.\n\nTo justify our analysis, we conducted experiments using ConvNext-T backbones pretrained on ImageNet. The results are presented below:\n| Train on                            | Test on    | mAP | 50  | 75  |\n|-----------------------------------|------------  |-----    |----- |----- |\n| SARDet-100K                       | SARDet-100K| 53.2| 86.3| 58.1|\n| DOTA                              | DOTA       | 45.2| 70.4| 49.1|\n| SARDet-100K + DOTA                | SARDet-100K| 51.9| 83.2| 56.7|\n| SARDet-100K + DOTA                | DOTA       | 42.4| 67.1| 46.1|\n| (DOTA pretrain) SARDet-100K     | DOTA       | 54.8| 87.1| 59.8|\n\nThe table demonstrates that joint training on SARDet-100K and DOTA results in a decreased mAP for SARDet-100K (from 53.2 to 51.9). In contrast, our proposed pretraining on DOTA before training on SARDet-100K improves the performance (mAP of 54.8). Additionally, the DOTA dataset's performance also drops significantly with joint training, supporting our claim.\n\nWe have incorporated additional recent works for comparison in Table 5 to provide a more comprehensive evaluation. The updated table is shown in Table R1 (in the rebuttal pdf).", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:
We add Figure R1, comprehensive visualizations of different handcrafted feature descriptors (including HOG, Canny, GRE, Haar and WST) to enhance clarity and accessibility.

We revise the main paper Table 5 to Table R1 (Table for comparison of the proposed MSFA with previous state-of-the-art methods on SSDD and HRSID datasets):  We incorporate additional recent works for comparison to provide a more comprehensive evaluation.

Meta-review: All reviewers recognize the contribution of this submission. After rebuttal, the authors provide further clarification of the issues mentioned by the reviewers. Now, all reviewers have no further issue for the submission. The AC agrees the reviewers' comments and learns toward an acceptance for this paper.
Decision: Accept (spotlight)