A Spatial Cross-Scale Attention Network and Global Average Accuracy Loss for SAR Ship Detection

Abstract

A neural network-based object detection algorithm has the advantages of high accuracy and end-to-end processing, and it has been widely used in synthetic aperture radar (SAR) ship detection. However, the multi-scale variation of ship targets, the complex background of near-shore scenes, and the dense arrangement of some ships make it difficult to improve detection accuracy. To solve the above problem, in this paper, a spatial cross-scale attention network (SCSA-Net) for SAR image ship detection is proposed, which includes a novel spatial cross-scale attention (SCSA) module for eliminating the interference of land background. The SCSA module uses the features at each scale output from the backbone to calculate where the network needs attention in space and enhances the features of the feature pyramid network (FPN) output to eliminate interference from noise, and land complex backgrounds. In addition, this paper analyzes the reasons for the “score shift” problem caused by average precision loss (AP loss) and proposes the global average precision loss (GAP loss) to solve the “score shift” problem. GAP loss enables the network to distinguish positive samples and negative samples faster than focal loss and AP loss, and achieve higher accuracy. Finally, we validate and illustrate the effectiveness of the proposed method by performing it on SAR Ship Detection Dataset (SSDD), SAR-ship-dataset, and High-Resolution SAR Images Dataset (HRSID). The experimental results show that the proposed method can significantly reduce the interference of background noise on the ship detection results, improve the detection accuracy, and achieve superior results to the existing methods.

LRMSNet: A New Lightweight Detection Algorithm for Multi-Scale SAR Objects

“…Tang et al [30] proposed a Pyramid Pooling Attention Network (PPA-Net) for SAR multi-scale ship detection. Zhang et al [2] proposed a spatial cross-scale attention network (SCSA-Net) for SAR image ship detection, which includes a novel spatial cross-scale attention (SCSA) module for eliminating the interference of land background. When dealing with issues like complex environmental backgrounds, intensive targets, and numerous small targets, the performance of these models may not be optimal.…”

“…In recent years, object detection has garnered increasing attention in the field of computer vision. The utilization of SAR imagery for object detection has emerged as a prominent research direction, finding diverse applications in defense and civil fields such as marine development, maritime transportation management, and maritime safety monitoring [2,3].…”

SAR Image Ship Target Detection Based on Receptive Field Enhancement Module and Cross-Layer Feature Fusion

“…However, combining these two methods requires a considerable amount of experimentation and tuning to achieve optimal performance. Zhang et al [23] proposed a detection network that includes a novel Spatial Cross-Scale Attention (SCSA) module. This innovative module utilizes features from each scale of the backbone's output to calculate the network's spatial focal points, aiming to eliminate interference from noise and complex land backgrounds.…”

DSF-Net: A Dual Feature Shuffle Guided Multi-Field Fusion Network for SAR Small Ship Target Detection

“…In order to enhance the fusion of multi-scale information and quantify the disparity between predictions and ground truth, several studies have sought to improve the loss function. For example, Zhang et al [29] introduced the Global Average Precision (GAP) loss to address the issue of prediction score shifting, while Xu et al [30] proposed the TDIOU loss to more accurately measure the positional relationship between predictions and actual ship targets. Despite the considerable efforts devoted by previous researchers to SAR ship target detection, several crucial aspects have been overlooked.…”