# Spatial Shape-Aware Network for Elongated Target Detection
## 2 Literature Review
To address this, AlignDet proposes the ROI-Conv module, which replaces standard convolutions by predefined convolutional offsets at specific locations on the feature map. This enables single-stage detectors to perform dense feature alignment similar to two-stage detectors. ReDet ([PERSON] et al., 2017) incorporates a rotation-equivariant network into the backbone, followed by a new rotation-invariant RoI alignment (RiRoI Align) method, that distorts and aligns region features for a better extraction of directional information. RADet ([PERSON] et al., 2017) identifies the high efficiency of the traditional mask branch in detecting multi-directional targets, utilizing the mask branch to predict the shape information of the target for generating rotational bounding boxes without the need for predefined rotational anchor boxes, thereby reducing computational load. In terms of regression optimization, APE ([PERSON] et al., 2017) uses two 2D periodic vectors to represent angular information in rotated bounding boxes, simplifying the angle computation process. A new cascading R-CNN method was subsequently proposed, employing a length-independent IoU (LIIoU) to detect long target objects. This allows detectors that only cover part of the target in the first stage to be considered positive samples, thereby generating longer bounding boxes and improving the detection of high-aspect-ratio rotated targets. PolarDet ([PERSON] et al., 2018) points out that traditional parameter regression methods can cause a significant drop in the network's convergence performance. To address this, it introduces a rotational bounding box representation using polar coordinates, utilizing multiple angles and short-polar-diameter ratios to represent the target. This approach allows the network to increase the angle loss and avoid the convergence performance degradation caused by the sharp variations in target size and aspect ratio. PIoU ([PERSON] et al., 2017) highlights that the loss calculated by the traditional five-parameter method is inconsistent with the IoU value. Specifically, even when the IoU value differs significantly, the loss can remain the same due to the excessive optimization of the angular error. Therefore, PIoU computes the IoU pixel-by-pixel, improving the consistency between the loss and IoU values. In terms of label assignment, SASM strategy ([PERSON] et al., 2018) proposes shape-adaptive selection (SA-S) and shape-adaptive measurement (SA-M) strategies, using a new decision threshold based on shape information to dynamically select samples and evaluate the potential of positive samples based on normalized shape distances, thus distinguishing the potential of positive samples more effectively and considering the target's shape information in label assignment. RFLA ([PERSON] et al., 2018) models the receptive field of each feature point as a Gaussian distribution and introduces a novel receptive field distance (RFD) to directly measure the similarity between the Gaussian receptive field and the target object. Based on the RFD, the method designs a hierarchical label assignment (HLA) module. This approach ensures that the distribution of feature points aligns more closely with the actual distribution of the target, thereby improving the effectiveness of detecting small targets. TOOD [28] redefines the label assignment scheme and loss function to provide training signals that incorporate both classification and regression information. These signals are then passed to a new detection head to compute interaction features for the classification and regression tasks. This allows for the alignment of the spatial distributions of features learned by both tasks from the convolutional layers, thus better coordinating the two tasks and obtaining the optimal anchor box for both tasks.