# Spatial Shape-Aware Network for Elongated Target Detection
## 2 Literature Review  
Due to the current limitations of convolutional structures, such as insufficient feature extraction and the inability of predefined boxes to accurately describe the pose, shape, and distribution of targets, existing deep learning detectors face difficulties in precisely and compactly locating targets with complex poses, irregular shapes, and varying scales. To address these issues, current research focuses on several aspects of improvement, including more accurate feature extraction, candidate box generation and feature alignment, regression optimization, and label assignment. The advantages and disadvantages of the relevant detection methods, as well as the application stage analysis, are shown in Table 1.  
In the field of more precise feature extraction and representation for targets, deformable convolution [13] has significantly improved the CNN's ability to model geometric transformations by introducing additional offsets that alter the spatial sampling positions within the module. This approach learns the offsets from existing task supervision information, eliminating reliance on supplementary information, and has shown better performance in complex visual tasks. [PERSON] et al. [14] introduced an adaptive optimization network comprising a feature selection module (FSM) and a dynamic refinement head (DRH), aimed at identifying rotated and closely arranged targets. FSM facilitates the calibration of neurons' receptive fields based on the target's configuration and orientation, which ensures accurate feature routing to the detection head. Meanwhile, DRH equips the model to adjust and make predictions dynamically in response to the characteristics of each object, enhancing adaptability in reasoning for unique targets. [PERSON] et al. [15] proposed an innovative approach to multi-scale object detection. Firstly, to enhance the diversity of receptive fields, they redesigned the feature extractor. This is followed by a multi-scale object candidate region generation network (MS-OPN) that generates candidate regions from intermediate layers with different receptive fields. A refined object detection network (ADDN) then processes these regions based on fused feature maps, resulting in stronger responses for small and dense objects. R3 DET [16] introduces a feature refinement module (FRM) that realigns features by reconstructing the feature map, mitigating the sensitivity of IoU to changes in rotational angle and thus improving performance in dense scenes and tasks with high aspect ratios. [PERSON] et al. (2017) applied two regularizers to CNN features. The rotation-invariant regularizer forces the CNN feature representations of training samples before and after rotation to be closely mapped, achieving rotational invariance. The Fisher discriminative regularizer constrains CNN features to have smaller intra-class variance and larger inter-class separation, thereby enhancing the extraction of class-specific information. In the area of candidate box generation and feature alignment, RRPN ([PERSON] et al., 2017) improves upon Faster R-CNN by proposing a novel framework that generates slanted anchor boxes during the initial stage and adjusts the angles during learning for bounding box regression, improving the detection of rotated objects. However, this approach struggles with multi-angle, variably sized, and densely distributed RS targets. Guide Anchor ([PERSON] et al., 2018) employs an anchor box generation module with position and shape prediction branches to generate object position probability maps and object shapes for high-probability regions. Additionally, it introduces a feature-adaptive module that uses predicted offsets for deformable convolutions to address feature inconsistency, thus enabling more efficient handling of complex visual tasks in intricate scenes. AlignDet ([PERSON] et al., 2017) points out that Im2 col operation in single-stage detectors is essentially a special form of RoIAlign that implicitly aligns bounding boxes, but is inefficient.