# Spatial Shape-Aware Network for Elongated Target Detection
## 1 Introduction  
Remote sensing (RS) detection, which obtains valuable information from visible light RS imagery captured by devices such as drones, is a key area of current research. It has broad applications in various fields, including surface ecological status detection, geological change detection, structural health monitoring, and intelligent transportation. The rapid advancement of UAV technology, coupled with the increased performance of graphics processing units, has been a growing demand for processing RS imagery in recent years. Consequently, neural network-based object detection algorithms have gained significant attention and widespread application. Existing RS detection frameworks can be broadly categorized into single-stage and two-stage detection. Single-stage detection uses predefined anchor boxes for rotated targetdetection, which requires a large number of anchor boxes. Convolutional operations are then applied to extract image features, followed by regression and classification to determine the target's position. Single-stage algorithms, including YOLO [1] and SSD [2], have been the starting point for many subsequent improvements. FE-YOLOv5 [3] introduces a global attention mechanism to reduce information loss and capture more discriminative features for small targets. It also employs a spatial attention module that uses sparse sampling and adaptive filtering to address the issue of small targets being overwhelmed by background noise in RS detection. MRFF-YOLO [4] method introduces a multi-receptive field algorithm and the Res2 Net and DenseNet modules, which represent multi-scale features with finer granularity at each layer, increase the receptive field range, and enhance feature propagation, thereby improving the detection capability of small targets in RS scenes without adding depth to the network. PAG-YOLO [5] incorporates an attention mechanism to highlight the importance of specific target categories or regions by adjusting the resource allocation weights, demonstrating superior performance in complex scenes or when there are many object categories. Reppoints [6] discards the traditional anchor box approach and directly regresses the target boundary points for localization. In two-stage detection, potential candidate regions are first screened, followed by preliminary regression and classification. Positive candidate regions are then selected and passed to pooling layers for feature extraction, leading to refined regression and classification. R-CNN [7] is a representative two-stage algorithm that initially generates candidate regions through a selective search and then performs scale normalization, and finally, features are extracted for the classification of the support vector machine and the regression of the bounding box. [PERSON] et al. [8] proposed an improved NMS module that does not directly discard detection boxes with a high IoU with the optimal box, but rather penalizes their confidence scores, allowing them to be considered in subsequent selections. This approach helps retain small targets in RS detection in overlapping regions, reducing false negatives. [PERSON] et al. [9] optimized sensitivity to small and partially occluded targets through a multi-layer feature fusion mechanism. They combined features from different depths via skip connections, which improved detection accuracy for partially occluded targets in RS imagery. In the context of traditional convolutional neural networks (CNNs), the primary limitation in detecting elongated targets in remote sensing imagery arises from the mismatch between detection network and the varying geometries of elongated targets. These anchor boxes are typically designed for objects with standard aspect ratios and fixed orientations, which makes them ineffective in accurately capturing the features of elongated and rotated objects. This misalignment leads to poor feature extraction and the failure to capture critical shape and directional information, significantly reducing the detection accuracy for elongated targets.