Self-Guided Low Light Object Detection Framework
Authors: Gwangik Shin
Jaeha Song
Soonmin Hwang
Venue: ICLR 2026 Poster (2026)
Keywords: Low light object detection
Self-guided training
No additional inference cost
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: Self-guided object detection framework for low light environment

Abstract: Object detection in low-light environments is inherently challenging due to limited contrast and heavy noise, both of which significantly degrade feature representations. In this paper, we propose a novel self-guided low-light object detection framework that effectively addresses these issues without introducing additional parameters or increasing inference time. Our method incorporates a detachable auxiliary pipeline during training, consisting of an image enhancement module and a denoising module, followed by a Fourier-domain fusion block. This pipeline improves the feature representation of the detector's backbone, enhancing its robustness under low-light conditions. Importantly, at inference time, our method incurs no additional computational cost compared to the baseline detector while achieving substantial performance improvements. Extensive experiments on widely used low-light object detection benchmarks, such as DARK FACE and ExDark, demonstrate that our method achieves state-of-the-art performance. Notably, experiments on the nuImages dataset show that our approach can outperform domain adaptation methods—especially when a large domain gap between source and target domains is inevitable in the real-world applications—highlighting its practical effectiveness. Code is available at https://github.com/gw-shin/SGLDet.

Published as a conference paper at ICLR 2026
SELF-GUIDED Low LIGHT OBJECT DETECTION FRAMEWORK
Gwangik Shin, Jaeha Song, Soonmin Hwang Department of Automotive Engineering Hanyang University Seoul, South Korea
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ABSTRACT
Object detection in low-light environments is inherently challenging due lim- ited contrast and heavy noise, both of which significantly degrade feature rep- resentations. In this paper. we propose a novel self-guided low-light object de- tection framework that effectively addresses these issues without introducing additional parameters or increasing inference time. Our method incorporates a detachable auxiliary pipeline during training, consisting of an image enhance- ment module and a denoising module, followed by a Fourier-domain fusion block. This pipeline improves the feature representation of the detector's 's back- bone, enhancing its robustness under low-light conditions. Importantly, at infer- ence time, method incurs additional computational cost compared the baseline detector while achieving substantial performance improvements. Exten- sive experiments widely used low-light object detection benchmarks, such as DARK FACE and ExDark, demonstrate that our method achieves state-of- the-art performance. Notably, experiments on the nulmages dataset show that our approach can outperform domain adaptation methods eestecillly when a large domain gap between source and target domains is inevitable in the real- world applications opplications-highllghinn. highlighting its practical effectiveness. Code is available at https:/ / thub. com/ -igggwshhhh shin/ SGLDet. sch.nett..ee....
INTRODUCTION
As autonomous driving technology is rapidly advancing, the robustness of object detection algo- rithms in low-light conditions has become increasingly critical for reliable environment perception and safe driving. While sensor-fusion approaches incorporating LiDAR thermal cameras have been explored to handle nighttime conditions (Rashed et al., 2019; Lu & Lu, 2021), they inevitably incur additional hardware costs and computational overhead. Consequently, achieving effective low- light perception using only cameras remains both crucial and challenging. However, camera-bar based detection algorithms face inherent limitations, including excessive noise, blurred object boundaries, and distortions such as glare and reflections due to artificial light sources. These factors can sig- nificantly impair detection performance, thereby increasing the risk of nighttime traffic accidents. Enhancing object recognition under low-light environments has thus emerged as a key challenge in the development of autonomous driving systems.
Object detection in low-light scenarios faces several critical challenges, A common strategy is to apply low-light image enhancement (LLIE) techniques as preprocessing step improve visual quality (Chen Wei, 2018; Liu et al., 2021a; Ma et al., 2022a; Xu et al., 2020; Sun et al., 2022). How- ever, these methods are primarily designed for human perception, often prioritizing visual appeal over detection utility, and thus contribute little to actual performance improvement. Alternatively, specialized low-light detectors have been proposed (Sasagawa & Nagahara, 2020; Cui et al., 2024; Peng et al., 2024), but they typically introduce high complexity and longer inference times. Explicit denoising, on the other hand, is rarely adopted during inference: while strong denoisers (Lee et al., 2022; Pan et al., 2023) effectively suppress noise, they also blur structural details and ineur
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heavy latency, making them unsuitable for real-time deployment. These limitations highlight the need for a new paradigm that can address noise while preserving both speed and accuracy.
To address these limitations, propose novel framework that integrates a self-guided mechanism into a baseline object detector, enhancing low-light detection without introducing additional infer- ence overhead. Specifically, our approach utilizes only the input low-light image itself to estimate supervisory target image that improves feature representation in the detector's ' backbone. The self- guided block addresses two key challenges in low-light scenarios: insufficient brightness and high noise. To tackle this issues, we adopt separate self-supervised modules for image enhancement and noise suppression, then merge their outcomes in the Fourier domain to produce supervisory signal that preserves both structural and perceptual cues. This detachable auxiliary task, guided by the fused target, enables the baseline detector to adapt to the characteristics of low-light data. Importantly, all these processes are applied only during training, keeping the inference time detector unchanged and incurring no additional computational cost. The main contributions are summarized as follows:
We present new framework design for low-light object detection that strategically employs de- tachable auxiliary supervision during training without inference-time computational overhead.
We incorporate enhancement and denoising into low-light detection through Fourier-domain fu- sion that combines denoised amplitude components with enhanced phase components, allowing our framework address noise while preserving structural details.
Our framework leverages sepe-suue modules that operate solely on low-light images, elimi- nating the requirement for paired ground-truth data and enabling adaptation to diverse nighttime environments.
Experiments across multiple datasets and detectors demonstrate the effectiveness and general- izability of the proposed approach, showing significant performance improvements in different low-light detection scenarios.
2 RELATED WORK
2.1 LOW-LIGHT IMAGES
Low-Light Image Enhancement Deep learning- based Low-Light Image Enhancement (LLIE) has been explored as a crucial approach to improving visual quality. Many methods rely on super- vised learning (Chen Wei, 2018: Chen et al., 2018; Zhang al., 2019; Wu et al., 2022: Cai al., 2023; Zhou et al., 2024), requiring paired low-light and ground-truth normal images for training. While these methods achieve high scores on image quality metrics, they are impractical in real-world outdoor scenes with dynamic objects, where ground-truth images are unavailable. To overcome this limitation, zero-reference methods have been introduced (Zhang et al., 2020; Jiang et al., 2021; Liu et al., 2021a; la; Li et al., 2021). incorporating non ron-reference loss functions without paired supervision such as estimating enhancement curves (Guo et al., 2020) or introducing self-calibration for illu- mination estimation (Ma et al., 2022a). However, most LLIE methods focus more on visual quality rather than downstream vision tasks such as object detection. In low-light conditions, they often amplify noise or blur object boundaries, leading to performance degradation of detection models.
Image Denoising Image denoising is a crucial process for reducing high noise levels in low-light environments. Supervised methods train models using clean-noisy image pairs (Zhang et al., 2017: Anwar & Barnes, 2019; Yue et al., 2019; Kim et al., 2020; Yue et al., 2020; Liu et al., 2021b), but collecting such data across diverse scenes scenes-especiee especially in outdoor driving scenarios with dy- namic objects-is impracticable. To address this, self-supervised methods (Krull et al., 2019; Huang et al., 2021: Wu et al., 2020; Lee et al., 2022; Pan al., 2023; Chen et al., 2024) leverage Blind- Spot Networks (BSN) (Krull et al., 2019), enabling noise mitigation without paired supervision. AP-BSN (Lee et al., 2022) removes spatially correlated noise using pixel-shuffle downsampling. SDAP (Pan et al., 2023) incorporates inter-sample differences, and knowledge distillation is intro- duced to achieve high performance with lightweight networks (Chen et al., 2024). Nevertheless, most denoising approaches focus solely on noise reduction, often introducing blurring or high la- tency, which makes them unsuitable for object detection. This has led to denoising being rarely adopted in low-light detection, gap work specifically aims address.
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2.2 OBJECT DETECTION LOW-LIGHT CONDITIONS
One approach for low-light object detection is to employ Low-Light Image Enhancement mod- ule (Chen Wei, 2018; Chen et al., 2018; Zhang et al., 2019; Wu et al., 2022: Guo et al., 2020; Liu et al., 2021a; Ma et al., 2022a; Cai al., 2023; Zhou et al., 2024) as preprocessing step. However, since LLJE methods are optimized for visual quality rather than object detection, their effectiveness in detection tasks is often limited. This limitation has led jointly optimize the image processing pipeline with the detection task for better alignment (Hashmi et al., 2023; Sun et al., 2022; Ma al., 2022b; Wang et al., 2022; Qin al., 2022). Another approach focuses on modifying the detector architecture (Sasagawa & Nagahara, 2020; Hong et al., 2021; Cui et al., 2024; Peng al.. 2024) to improve performance under low-tight conditions. These methods introduce multi- model merging (Sasagawa & Nagahara, 2020), feature enhancement modules (Peng et al., 2024), and illumination-awaa representation learning (Cui et al., 2024). However, both approaches intro- duce additional computational overhead compared to the baseline detector, limiting their practicality for real-time applications.
Alternatively, domain adaptation techniques (Hong et al., 2021; Wang et al., 2021; Cui et al., 2021; Du et al., 2024) aim to transfer knowledge learned from a normal-light source domain to a low- light target domain. Various methods employ strategies such multi-task autoencoding (Cui al., 2021), low-light image synthesis (Hong et al., 2021), and Retinex based adaptation (Du et al., 2024). However, their efffcctiveness heavily depends target domains. Although these methods also do not introduce additional inference overhead, their performance often deteriorates in scenarios with substantial domain gaps, such nightrime driving, due to the source domain bias.
the degree of similarity between the source and
METHOD
3.1 SELF-GUIDED LOW-LIGHT DETECTION FRAMEWORK
Existing approaches to low-light object detection commonly rely on complex image enhance- ment preprocessing step, customized detector architectures, domain adaptation strategies. Enhancement-basee methods often amplify noise and increase complexity at inference stage, mod- ified detectors typically require architectural changes and additional effort during training, and do- main adaptation techniques exhibit inconsistent performance across datasets due to varying domain gaps. In contrast, our approach directly uses only the original low-light image input during infer- ence, avoiding both information loss and extra computational cost. To achieve this, we introduce a detachable auxiliary pipeline, used only during training, which estimates target image that serves as supervision for the detector's ' backbone.
The key idea is illustrated Figure 1. The detector consists of a backbone (B) and a head (H),, both of which receive the unmodified low-light input image (r). In parallel, the auxiliary pipeline pro- cesses x through an enhancing module (E) and a denoising module (D), whose outputs are combined via bourier-based fusion. This produces supervision signal that guides the backbone via multi-task learning. After training, the auxiliary pipeline is removed, resulting in no inference overhead.
3.2 AUXILIARY MODULES FOR SELF-GUIDED SUPERVISION
To guide the backbone network toward improved representations in low-light settings, we design target estimation pipeline that operates solely on the input image and is applied only during training. This pipeline consists of three self- ceff-supervised modules: an enhancing module, a denoising module, and a Fourier-based image fusion. The design explicitly addresses common low-light challenges such as insufficient brightness and high noise. All modules are retrained on the target dataset to best capture the characteristics of the given low-light domain.
Enhancing Module (E) To improve the overall brightness and contrast of the image, we incor- porate self-supervised lig-tight image enhancement network. This allows framework to be trained without the need for paired ground-trume bright images, making applicable to worldworld scenarios where only low-light images available. Low-light images inherently suffer from lim-
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Training Stage
X
D
Ap
Testing Stage
Leels
X
Bø
H
Oulpul
Go
x
x
Bg
H
det
Figure 1: Overview of the proposed framework. We propose auxiliary pipeline, depicted by dashed-line patterns, to generate a target image for self-guided supervision during training. This pipeline consists of a decoder (9), an enhancing module (E), denoising module (D), and Fourier- based image fusion (F,F-1) At the testing stage, the auxiliary pipeline is removed, maintaining the original inference cost of the detector while improving its performance in low-light environments.
ited photon counts, resulting in a low Signal-to-Noiss Ratio (SNR). While enhancement increases the signal strength of the image, also amplifies noise, illustrated in Fig. 2.
Denoising Module (D) To address noise-related challenges, we adopt a self-supervised denois- ing module. Since both image noise and perceptual details such edges reside in the high- frequency domain, the denoising process reduces noise levels but also risks suppressing structural details resails-rreulting in blurred or smoothed edges, illustrated in Fig. 2.
Fourier Transform (F) The Fourier transform F operates on a given single-channel image x of size H x W, converting into complex component X in the Fourier space.
11
W-1
X(u, = Σ Σ
æth, w/e 52ml -22ml
and inverse Fourier transform. denoted as F-1 reconstruct the original spatial domain image from its components of the frequency domain. In our pipeline, we apply the Fast Fourier Transform (FFT) (Nussbaumer & Nussbaumer, 1982) to each channel of an image independently, enabling a decomposition of frequency components across different spectral bands. As result of the Fourier transform, the complex component X (u,,) v) of image can be decomposed into its amplitude A(X (u, v)) and phase ((X(u, u)).
Alu, v) = =/ (R)
(2)
(T ((X((u,) v))
P(u, v) = taa
(3)
R(X(u, w))
Here, R(x) and [(x) represent the real and imaginary parts of complex value X(u, v), respectively.
3.3 TARGET IMAGE CONSTRUCTION VIA FOURIER FUSION
According to Fourier theory, the amplitude component A captures low-level information such as brightness, noise, and style, while the phase component P. preserves the structural and semantic details of an image (Piotrowski & Campbell, 1982; Xu et al., 2021). To retain the structural in- tegrity of the enhanced image while incorporating the stylistic properties of the post-enhancement wwwpeeed-afffee-enneeeeee
denoised image, we extract style from the enhancement denoised image offers an wew.iiinttooooorrettttt and supeessup amplitude, pro- viding a cleaner and more consistent source of stylistic information for fusion. (Fig. 2) FFT is applied independently each RGB channel, with amplitude and phase computed separately per channel. We then combine the phase of the enhanced image with the amplitude of the denoised image and apply inverse FFT (iFFT) to construct the final target image.
result because the post-
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Specifically, the final supervisory target image è is timated solely from the low-light input 2 via the aux- iliary pipeline: enhancement xº E(x), denoising enhanced image 2F+D D (xx),, ) followed by Fourier decomposition. We extract the phase of the enhanced image P(xf) and the amplitude of the denoised image A(x*+D), then reconstruct the fused target via inverse FFT:
E
F
= iFFT(AA E (P(())
(4)
FE-D
Fused Image (x)
The fused image serves as a dense, structure- preserving supervision that complements sparse detec- tion labels and helps the backbone learn robust features under low-light and noisy conditions. Our framework tackles two key obstacles to using enhance- ment and denoising in detection: the auxiliary pipeline avoids inference-ting timenneee-meeeeee cost, and Fourier fusion helps preserve boundaries and semantics.
Figure 2: Fourrierbasse Image Fusion.
3.4 FRAMEWORK INTEGRATION AND LOSS
Our self-guided detection framework is designed for seamless integration into standard object de- tectors without requiring any architectural modifications. During training, we attach the proposed detachable auxiliary decoder (G) the detector's backbone via skip connections, enabling multi- task learning. Each decoder layer receives feature maps from the corresponding backbone layer, facilitating dense supervisory signal with precise spatial alignment.
This dense supervision provided by the fused target image å generated solely from the input low- light image using Fourier Fusion module, described in Section 3.3. Since the features from detector's backbone are used to predict this target image through the auxiliary decoder G. the self- guided reconstruction loss (Leetf) encourages the backbone to retain structural and semantic that are often diminished in low-light conditions. Moreover, given that standard object detection targets (i.e., bounding boxes) provide sparse supervision, the self-guided reconstruction loss serves as dense complementary signal. By enriching the overall supervision during training, the proposed framework enables the detector's 's backbone to learn more robust representations for object detection in challenging low-light environments.
Self-Guided Supervision Loss For the self-guided loss, which provides dense pixel-wise self- guidance to the detector's backbone, we use the mean squared error (MSE) between the decoder output and the fused target image à:
Laelf = - 111
(5)
The total training loss thus defined as:
Liotal Liet + X Leels
(6)
where À is weighting factor that balances detection and self-guidance.
EXPERIMENTS
In this section, evaluate framework three public datasets with multiple detectors to demon- strate its effectiveness and generalization capability. We conduct experiments on face detection (Sec. 4.2), multi-class object detection using YOLOv3 (Redmon, 2018) (Sec. 4.3), and nighttime ob- ject detection for autonomous driving (Sec. 4.4). We further provide an in-depth analysis (Sec. 4.5) and ablation studies (Sec. 4.6).
4.1 IMPLEMENTATION DETAILS
Our model is trained using the proposed Self-Guided Low-light Detection (SGLDet) framework, with SCI (Ma et al., 2022a) the enhancing module (E) and SDAP (Pan et al., 2023) as the de-
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noising module (D). Since both modules able to be trained in self-supervised manner, they retrained on the target dataset. For the auxiliary detachable decoder (9), we employ a U-Net decoder architecture composed of transposed convolutions and double convolution blocks. To directly influ- ence all layers of the detector's backbone, each backbone layer connected to the decoder via skip connections, which are used only during training.
For benchmarking, we compare our framework against state-of-the-art LLIE methods (Liu et al., 2021a; Guo al., 2020; Li et al., 2021; Ma et al., 2022a; Cai al., 2023; Zhou al., 2024) and T2 (Cui et al., 2024), a dedicated low-light detector, that we reimplemented for evaluation. To ensure fair comparison, sulf-sueeeiiee LLIE methods, which can be trained from unpaired low- light images, were retrained on the target dataset. Meanwhile, supervised methods were trained on the L.L. dataset (Chen Wei, 2018), which provides paired ground truth normal images.
4.2 EVALUATIONS ON THE DARK FACE DATASET
In this experiment, we use DARK FACE (Yang et al., 2020) dataset, designed for low-light face de- tection. Since the labeled faces small in nighttime scens, this dataset is well-suited for evaluating detection models under challenging conditions.
Settings For the baseline detector, we employ DSFD (Li et al., 2019) with ResNet (He et al., 2016) backbone. Since objective is to evaluate detection performance in low-light conditions, all models are trained from scratch without using any pre-trained weights, except for initializing the ResNet backbone with its default weights. The DARK FACE dataset consists of 6,000 real-world low-light images, following the HLA-Face (Wang et al., 2021) split, where 5,500 images are used for training and 500 for validation. We optimize the model using SGD, with the first 10% of 100 epochs allocated for warming up. The maximum learning rate is set to 0.001, and experiments are conducted using cosine one-cycle scheduler. In our proposed approach, Lzels formulated with MSE, and its weight is scaled by a factor of 0.01. The computational cost of each method was measured on an NVIDIA H100 GPU. For fair comparison, supervised LLIE methods were trained with paired normal-light images from the LOLvI dataset, and the domain adaptation method DAI- Net was additionally pre-trained on WIDER Face (Yang al., 2016) as the cource-domant dataset and trained on the DARK FACE, following the protocol outlined by the original authors.
Results As shown in Table 1, our framework achieves the highest performance gain (+16.9 mAP) with no ad- ditional parameters, FLOPs, or inference time, high- lighting its practical effectiveness. Interestingly, super- vised LLIE methods trained on LOLv1 (68.1 and 65.9 mAP) are higher than self-supervised approaches re- trained on DARK FACE (from 61.1 1 to 65.7 in mAP), suggesting their better adaptation to the dataset's s low- light conditions. DAI-Net, domain adaptation method pre-trained on WIDER Face, also demonstrates com- petitive performance (68.5 in mAP) without inference- time overhead. However, our method ultimately outper- forms all competitors (Table Figure 3). By improving backbone features without extra modules or datasets, it achieves Mate-of-the-are accuracy.
Precision
0.6
76.6 OneS
63.5 DeL
65 7- Zuro-DCR
65.5- RUAS
61.1 1111-20o-DCPP
59.7 Baseline
0.2
Recall
Figure 3: PR curve the DARK FACE.
As shown in Fig. 4, our method reduces missed detections-espeet enhancing night-specific feature learning in low-light and noisy conditions. The results confirm the effectiveness of our framework for the detection in real-world scenes.
for small, distant faces-br
4.3 EVALUATIONS ON THE EXDARK DATASET
The ExDark dataset (Loh & Chan, 2019) is multi-class object detection benchmark widely adopted in the low-light detection research. Following the protocol established by MAET (Cui et al., 2021), adopt YOLOv3 as the baseline detector, which serves standard evaluation setup for this task.

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Table 1: Performance and computational cost comparison with state-of-the-art on DARK FACE.
Additional Dataset
Additional Costs
Category
Method
mAP
Params
FLOPs
TIME (ms)
Baseline Detector
DSFD (Li et al., 2019)
39.7
RUAŠ
65.5
(+5.8)
3,4
5.23
4.98
Zero- ACE-DCE (CVPR 2020)
63.7
(+6.0)
79.4 K
123.21 G
4,30
Self Supervised LLEE
Zero-DCE++ [TRAMI
61.1
(+1.4)
10.6
0.15
SCI
64.3
(44.6)
0.3
0.38
0.71
Retinexformer peCV
68.1
(+8.4)
1.61 M
385.88 G
43.73
Supervised LLIE
LOLv1
GLARE (820V
63.9
(+6.2)
71.72 M
74.26
746.72
Domain Adaptation
DAI-Net (CVPR 2014)
68.5
(+8.8)
WIDER Face
Self-Guuied framework
Ours
76.6
(+16.9)
Baseline
RUAS
Zero-DCE
SCT
Retinexformer
DAT-Net
Ours
Figure Qualitative comparisons on DARK FACE. More results can be found in the Supplementary.
By benchmarking our method on this representative setting, we aim to demonstrate the effectiveness and generalizability of our framework in comparison state-of-the-art approaches.
Settings We use YOLOv3-60S (Redmon, 2018) with Darknet-53 backbone as our baseline detec- tor. The ExDark (Loh & Chan, 2019) dataset consists of 7,363 images. Following the MAET (Cui et al., 2021) protocol, we use 5,896 images for training and 1,467 for validation, strictly adhering to their official split and training pipeline. Except for domain adaptation methods, all models including ours are initialized with COCO-pretrained weights for the YOLOv3 detector and finetuned on Ex- follow
Dark for 25 epochs, done in the MAET comparisons. For domain adaptation models, their respective pretraining strategies on COCO and perform 25-epoch finetuning on ExDark. We optimize all models using SGD, with 2,000 warm-up iterations and learning rate of 0.001, reduced by a factor of 10 at epochs 18 and 23, following the training schedule of MAET.
Results Table 2 presents the quantitative results of framework compared to existing meth- ods on the ExDark (Loh & Chan, 2019) dataset. Several self-supervised LLIE methods such as RUAS (Liu et al., 2021a) and Zero-DCE++ (Li al., 2021) degrade performance (-2.7 and -1.1 mAP, respectively). In contrast, SCI (Ma et al., 2022a) and Zero-DCE (Guo et al., 2020) show modest gains (+1.5 and +0.5 mAP), suggesting that lightweight enhancement can help but is not consistently reliable.
Supervised LLIE methods like Retinexformer (Cai al., 2023) and GLARE (Zhou et al., 2024), despite leveraging paired training data, fail to outperform the baseline (-0.9 and -1.4 mAP). Domain adaptation methods, MAET (Cui al., 2021) and DAI-Net (Du al., 2024), perform better (+1.3 and +1 mAP), benefiting from cross-domain training. Our framework achieves the highest gain of +2.2 mAP over the baseline, establishing a new state-of-the-an result on ExDark.
4.4 NIGHT OBJECT DETECTION FOR DRIVING SCENES
Object detection in nighttime driving scenarios is particularly challenging due to extremely low il- lumination and severe sensor noise. Unlike general low-light photography, where exposure can be adjusted, autonomous driving systems fixed camera settings for day-night consistency. This sults in nighttime images with severely degraded visibility and object boundaries. Moreover, the nighttime scenes in nulmages frequently exhibit intense headlight glare, high-frequency noise along

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Table 2: Quantitative results on the ExDark.
Table Experiments on nulmages.
Total Inference Time
Category
Method
mAP
Model
mAP
TIME (ms)
Baseline Detector
YOLOv3-608
76.4
Training from scratch Night (w/o pretraining)
Retinex Decompositin
T2 (0241
75.5
YOLOv8-m
52.4
5.88
170.1
+ T2
51.1
12.56
79.6
RUAS 2021]
73.7
+ SCI
53.5
8.12
123.2
Zem-DCB (CVPR 2000)
76.9
Self Supervised LLIE
Retinexformer
52.1
129.24
7.3
Zero-DCBs (TPAMI 2011)
75.3
+ GLLARE
51.7
4896.72
0.2
[CVPR 2022]
77.9
YOLOv8-m + Ours
55.3
5.88
170.1
Retinexformer 2023)
75.5
Supervised LLIE
Pretruined on Daytime - Fiuetamed on Night (w/ pretraining
GLARE (RCCV 2014)
75.0
YOLOv8-m
56.4
MAET ICCV 2021]
77.7
Domain Adaptation
MAET
53,8
5.88
170.1
DAI-Net (CVPR (CVPR2004)
78.3
DAI-Net
54.4
Self Guided framework
Ours
78.6
YOLOvS-m + Ours
58.0
5.88
170.1
road boundaries, and pronounced illumination imbalance between foreground objects and the back- ground, further complicating reliable feature extraction.
To assess our framework's performance in these conditions, we use the nulmages (Caesar et al., 2020) dataset, chosen for its authentic nighttime driving images. We measure both detection accu- racy and inference speed to verify its suitability for real-time driving applications.
Settings To evaluate the practicality of our method, we adopt YOLOv8-m (Jocher et al., 2023), modern real-time object detector, as the baseline. The nulmages dataset primarily consists of day- time scenes, with only limited number of nighttime images. From this dataset, we extract 4,485 nighttime images and split' them into 3,591 for training and 894 for validation. Due to severe class imbalance-egg. 6, 150 instances of car but only 66 of bicycle-we focus on four categories with sufficient samples: car (6,150), pedestrian (1,665), traffic cone (1.962), and barrier (2,202).
We consider two training settings: (i) training from scratch using only nighttime images, and (ii) finetuning nighttime images after pretraining daytime nulmages. The second setting is crucial for comparing with domain adaptation methods like MAET and DAI-Net, as they require a source (daytime) domain for pretraining. Our framework and the baseline were tested in both settings, while the domain adaptation methods were evaluated only in the second, inherently. All models are trained under identical settings, and inference speed is measured on an NVIDIA H100 GPU.
Results Table presents the results on nighttime driving scenes from the nulmages dataset. Our framework achieves the best per- formance in both training scenarios while maintaining real-time inference speed. When trained from scratch nighttime images, method surpasses the YOLOv8-m bascline by +2.9 mAp with additional inference cost. Notably, most enhancement or decom- position methods fail to improve performance (e.g.. -1.3 with T2, -0.7 with GLARE) and lose real-time capability (e.g., 0.2 FPS with GLARE). In contrast, our method enhances detection accuracy without compromising speed (170.1 FPS). highlighting its efficiency for real- world deployment. In the domain adaptation setting (pretraining on daytime data, finetuning on night images), our framework again delivers the highest accuracy, outperforming both the baseline (+2.6 mAP) and other methods like MAET (-2.6 mAP) and DAI-Net (-2.0 mAP). These results high- light a critical limitation of these techniques for driving scenarios with large domain gaps: they often fail to adapt from a source domain (daytime) to vastly different target domain (nighttime). Overall, our approach demonstrates state-of- the-art performance on this challenging real-world benchmark, achieving robust generalization without sacrificing computational efficiency.
YOLOV8
MAET
DAI-Net
Ours
Figure 5: Qualitative comparison on nulmages with domain adaptation.
Since the original nulmages validation set does not include night scenes, we resplit the dataset.
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Froed
bituradd-- Censeal-Crrsaad Carobad
Charcad-Cerbbur Fued
Feature
Results
(a) Low level information (b) Noise level distribution
Figure 6: Analysis of Fourier-based image fusion in the spatial domain using t-SNE. (a) Visualizes low-level information, including structural and semantic details, while (b) illustrates the distri- bution of noise levels.
DSFD
Ours
Figure 7: Feature visualization.
4.5 ANALYSIS OF THE SGLDET FRAMEWORK
Target Image Analysis To verify whether the fused target image in Sec 3.3 retains low-level spatial information while exhibiting noise level similar to that of the denoised image, we perform an analysis using t-SNE (Van der Maaten & Hinton, 2008). We extract low-level information using HOG features (Dalal & Triggs, 2005) and assessed noise levels by subtracting image. The results indicate that the fused target image retains the low-level traits of the enhanced image (6-a) while its noise level matches that of the denoised image (6-b).
Backbone Feature Analysis Since the proposed framework incorporates auxiliary task nected to the backhone during training, it enhances feature representation in low-light conditions. Fig. 7 visualizes feature maps from the same ResNet backbone for both the baseline and our method. With only sparse box-level supervision, the baseline exhibits weak activations, often missing clear object-background separation. In contrast, our self-guided framework yields stronger activations in the person region and sharper boundary contrast, indicating improved localization and structure awareness". These results confirm that the fused pixel-level guidance helps the backbone learn more discriminative and robust features, ultimately improving detection performance.
Sensitivity to the Weighting Hyperparameter À We further analyze the sen- sitivity of the framework to the weighting factor X between Ldet and Lyeir on the DARK FACE dataset. Small values of X (e.g.. 10-3) provide only limited dense guidance, resulting in moderate gains. Performance peaks at À = 0.01, in- dicating that balanced contribution from reconstruction supervision most effec- tively complements the detection objective. In contrast, large values (A 2 0.05)
Table 4: Sensitiv- ity analysis.
A
mAP
0.0001
72.5
0.0010
74.0
0.0100
76.6
cause the reconstruction loss to dominate, degrading detection features and lead-
0.0500
67.3
ing to unstable optimization when À becomes too large. These results suggest that \ should remain within moderate range for stable and effective training.
0.1000
61.6
4.6 ABLATION STUDIES
Ablation on Framework Components Table
Table 5: Ablation on framework components.
presents ablation study on target image guidance ing DARK FACE dataset. First, when using enhancing- only image (i.c.. processed solely through the Enhanc- ing Module), brightness and contrast are improved, re- sulting in +13.2 mAP increase over the baseline. How- ever, as shown in Fig. 2, this approach also amplifies noise. To mitigate this, applying a Denoising Module after enhancement effectively suppresses noise, but at the cost of some boundary detail loss, leading to a slight performance drop to 72.2 mAP. Finally, leveraging Fourier-based Image Fusion, which combines both results in the Fourier domain, enables the target image to retain structural details
Target Image
Method
mAP
Fusion (E, D)
DSFD
59.7
72.9
72.2
Ours
76.6
Further analysis improved localization provided in the Supplementary Material.

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Published as a conference paper at ICLR 2026
while effectively suppressing noise, achieving 76.6 mAP. This represents an +16.9 mAP improve- ment over the baseline, validating the effectiveness of each component.
Ablation on Module Choices addition to the
Table 6:
Ablation study on module choices.
component wise analysis, investigate how sen- sitive our framework is to the specific choices the
Enhancer (E)
Denoiser (D)
mAP
Enhancer (E) and Denoiser (D) using various combi- nations in Table 6. Even a naive setting with gamma correction and Gaussian blur already yields large improvement over the baseline (59.7 mAP - 70.9 mAP), indicating that the gain does not rely on so- phisticated modules. When employing the denoiser to SDAP, self-supervised enhancers such as Zero- DCE and SCI, which can be trained directly on the target dataset, achieve higher performance than the supervised Retinexformer that requires paired illumination labels, suggesting that our self-guided pathway is naturally aligned with target-adaptable enhancement. Conversely, applying the enhancer to SCI and varying the denoiser shows that Gaussian-trained Restormer (Zamir al., 2022) variants are highly sensitive to the assumed noise level a and can even underperform a simple median blur when the noise model is mismatched, whereas Restormer trained real noise dataset (Abdelhamed et al., 2018) and SDAP provide more stable improvements because both rely on more realistic noise assumptions. SDAP achieves the best mAP through self-supervised training on the target domain. Overall, these trends demonstrate that while stronger enhancement or denoising modules can lead incremental accuracy gains, the primary performance improvement consistently originates from the detachable self- guided pathway itself, indicating that our framework remains robust and flexible with respect to module choice.
Gamma correction
Gaussian blur
70.9
Retinexformer Zero-DCE
72.5
SDAP
73.8
SCI
76.6
Median blur
73.6
Restormer (a = 15)
75.0
Restormer (a 25)
73.0
SCI
Restormer (a = 50)
73.0
Restormer (real-noise)
76.0
SDAP
76.6
self-guided
5 CONCLUSION
In this paper, we proposed self-guided framework for enhancing object detection performance in low-light environments. Our approach incorporates auxiliary supervision components during train- ing while maintaining the original computational cost at inference time, distinguishing from meth- ods that require additional inference modules or depend on domain adaptation strategics. The frame- work integrates self-supervised modules for enhancement, denoising, and Fourier-domain fusion to improve backbone feature representations under challenging lighting conditions. Experiments across three standard low-light benchmarks (DARK FACE, ExDark, and nulmages) demonstrate that our framework consistently outperforms existing approaches, achieving new state-of-the-art results without modification to the original detection architecture at inference. dataset, which contains nighttime driving scenes, framework outperforms both enhancement- based methods that incur runtime overhead and domain adaptation approaches in scenarios with large domain gaps. These results demonstrate that training-time supervision strategy provides an effective solution for low-light object detection in practical applications.
the nulmages
ACKNOWLEDGMENTS
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. RS- RRS-2024-00409499),
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LLM USAGE DISCLOSURE
We used large language model (LLM) solely to aid in polishing the writing of this paper. All research ideas, methods, experiments, and analyses are the sole work the authors.
14


Reviews:
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The authors claim this approach improves feature representation for low-light scenes, achieving state-of-the-art results on several benchmarks (DARK FACE, ExDark, nuImages) without adding any computational cost during inference.", "questions": "Please refer to weakness part.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "+ The manuscript is well-represented and easy-to-follow.\n\n+ The auxiliary pipeline (enhancer, denoiser, fusion) is completely detached after training. This means the detector is identical in architecture, parameters, and speed to the baseline model, yet it performs significantly better.\n\n+ The effectiveness of the framework is validated across three different datasets and with multiple detector architectures.", "weaknesses": "+ The paper claims that performance stems from the framework design itself. However, in Table 5, using simple modules (Gamma Correction, Gaussian Blur) yields a 70.9 mAP, while advanced modules (SCI, SDAP) are required to reach the top performance of 76.6 mAP. This indicates the framework's effectiveness is tied to the quality of the chosen enhancer and denoiser. It would be better to see more variant of enhancer/denoiser pairs and more discussion for this.\n\n+ The model is trained with a multi-task loss combining sparse detection (L_det) and dense pixel-reconstruction (L_self). How is the sensitivity to the weighting hyperparameter $\\lambda$?\n\n+ Figure 4, Retienxformer -> Retinexformer. The authors should carefully check the manuscript to eliminate typos and grammarical errors.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "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": "PeMfptcH7m", "reviewer": "Reviewer_pa43", "summary": "This paper proposes a self-guided low-light object detection framework to improve detection performance under challenging lighting. The method utilizes image enhancement and denoising, and then the outputs are fused in the Fourier domain. The fusion is used to generate a dense pixel-wise supervision signal encouraging the detector's backbone to learn more robust low-light representations. Experiments are performed on DARK FACE, ExDark, and nuImages datasets, along with ablation and qualitative analyses.", "questions": "1. Did you analyze how signal-dependent noise, common in low-light shots, is distributed between the amplitude and phase components?\n2. Did you investigate whether the fusion process introduces frequency-domain artifacts, and if so, how were they handled?\n3. How does the proposed method perform when significant boundary artifacts are present in the input images?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. Fourier-domain fusion: The method’s technical core by combining the amplitude from a denoised image with the phase from an enhanced image using FFT/iFFT is grounded in signal processing principles and attempts to both preserve structure and suppress noise. \n2. Extensive evaluation: The experiments cover multiple datasets (DARK FACE, ExDark, nuImages) and detectors. Ablation studies isolate the contributions of each module, and Figure 4 offers qualitative insights.\n3. No inference overhead: The method is attractive for applications as it adds no complexity or latency at inference.", "weaknesses": "1. While the use of Fourier fusion is motivated by separation of amplitude and phase, the theoretical justifications for this separation, particularly in the context of low-light image statistics and deep feature learning, are not well explained and discussed.\n2. The mathematical details for the Fourier fusion are sometimes ambiguous, e.g., the precise computation of bi-level amplitude-phase combination per-channel, whether normalization occurs before/after fusion, whether channel alignment causes artifacts.\n3. The impact of severe boundary artifacts, non-Gaussian noise, or extreme illumination imbalance on the auxiliary fused target and final detector features is not explored.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "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": "v8S0FSQOZy", "reviewer": "Reviewer_rmxc", "summary": "This paper proposes a Self-Guided Low-light Object Detection Framework with a detachable auxiliary pipeline used only during training, which generates a high-quality supervisory target through an image enhancement module, a denoising module, and Fourier-domain fusion. Experimental results show that this method achieves state-of-the-art performance on multiple benchmarks, with a significant improvement on DARK FACE.", "questions": "In Section 3.3, the authors chose the serial strategy $x^{\\mathcal{E}+\\mathcal{D}} = \\mathcal{D}(x^{\\mathcal{E}})$. Why is the current serial strategy the superior choice? Why not choose a parallel fusion strategy (e.g., fusing $\\mathcal{P}(\\mathcal{E}(x))$ and $\\mathcal{A}(\\mathcal{D}(x))$, where $\\mathcal{D}$ operates on the original input $x$)?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "1. The designed auxiliary pipeline is activated only during training and is completely detached at inference time, which allows the model to achieve substantial performance gains while maintaining the exact same inference speed.\n2. The method leverages the properties of the Fourier transform and self-supervised training strategy, which is novel and sound.\n3. The writing is well-organized and easy to follow.\n4. Experiments show significant improvement on multiple benchmarks.", "weaknesses": "1. The paper repeatedly emphasizes \"zero inference overhead\", but completely omits any discussion of the cost during training-stage. The introduction of the auxiliary pipeline ($\\mathcal{E}, \\mathcal{D}, \\mathcal{G}$), retraining of $\\mathcal{E}$ and $\\mathcal{D}$ on the target dataset, and multiple Fourier transforms will clearly increase training complexity and time, but this trade-off is not quantified. \n2. The total loss function uses a hyperparameter $\\lambda$ to balance the main and auxiliary pipeline, but the paper lacks a sensitivity analysis for this crucial hyperparameter.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "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:

Decision: Accept (Poster)