Research on lightweight and feature enhancement of SAR image ship targets detection

The accuracy of ship targets detection in sythetic aperture radar images is susceptible to the nearshore clutter. The existing detection algorithms are highly complex and difficult to deploy on embedded devices. Due to these problems a lightweight and high-precision SAR image ship target detection algorithm CA-Shuffle- YOLO (Coordinate Shuffle You Only Look Once) is proposed in this article. Based on the YOLO v5 target detection algorithm, the backbone network is improved in two aspects: lightweight and feature refinement. The lightweight module is introduced to reduce the computational complexity of the network and improve the reasoning speed, and a collaborative attention mechanism module is introduced to enhance the algorithm's ability to extract the detailed information on near-shore ship targets. In the feature fusion network, weighted feature fusion and cross-module fusion are used to enhance the ability of the model to fuse the detailed information on SAR ship targets. At the same time, the depth separable convolution is used to reduce the computational complexity and improve the real-time performance. Through the test and comparison experiments on the SSDD ship target detection dataset, the results show that the detection accuracy of CA-Shuffle-YOLO is 97. 4%, the detection frame rate is 206FPS, and the required computational complexity is 6. lGFlops. Compare to the original YOLO v5, the FPS of our algorithm is 60FPS higher with the required computational complexity of our algorithm being only the 12% that of the ordinary YOLOvS. © 2024 Science Press. All rights reserved.