YOLO-SD: Small Ship Detection in SAR Images by Multi-Scale Convolution and Feature Transformer Module

As an outstanding method for ocean monitoring, synthetic aperture radar (SAR) has received much attention from scholars in recent years. With the rapid advances in the field of SAR technology and image processing, significant progress has also been made in ship detection in SAR images. When dealing with large-scale ships on a wide sea surface, most existing algorithms can achieve great detection results. However, small ships in SAR images contain little feature information. It is difficult to differentiate them from the background clutter, and there is the problem of a low detection rate and high false alarms. To improve the detection accuracy for small ships, we propose an efficient ship detection model based on YOLOX, named YOLO-Ship Detection (YOLO-SD). First, Multi-Scale Convolution (MSC) is proposed to fuse feature information at different scales so as to resolve the problem of unbalanced semantic information in the lower layer and improve the ability of feature extraction. Further, the Feature Transformer Module (FTM) is designed to capture global features and link them to the context for the purpose of optimizing high-layer semantic information and ultimately achieving excellent detection performance. A large number of experiments on the HRSID and LS-SSDD-v1.0 datasets show that YOLO-SD achieves a better detection performance than the baseline YOLOX. Compared with other excellent object detection models, YOLO-SD still has an edge in terms of overall performance.

Automated summary

As an outstanding method for ocean monitoring, synthetic aperture radar (SAR) has received much attention from scholars in recent years. Significant progress has been made in ship detection in SAR images, however, detecting small ships in SAR images remains challenging. To address this issue, we propose an efficient ship detection model named YOLO-SD, which improves accuracy by fusing feature information at different scales and optimizing semantic information. Experimental results show that YOLO-SD outperforms baseline YOLOX and other object detection models in terms of overall performance.