GS-YOLO: A Lightweight SAR Ship Detection Model Based on Enhanced GhostNetV2 and SE Attention Mechanism

Abstract

Synthetic Aperture Radar (SAR) imaging technology is crucial for maritime vessel monitoring. However, the inherent characteristics of SAR images, such as limited feature resolution and speckle noise, pose a series of challenges for ship detection. Although existing research has made great progress in improving the model detection accuracy, it often comes at the cost of reduced efficiency and increased computational complexity. To address this issue, we propose a lightweight SAR ship detection model based on YOLOv5s, named GS-YOLO. Firstly, we adopt C3GhostV2 modules as the lightweight backbone to enhance the computational efficiency which is based on GhostNetV2 and GConv. In the neck, Squeeze and Excitation (SE) attention is employed to strengthen the feature extraction ability for small targets and improve the SAR ship detection precision. Furthermore, we develop a novel XIoU loss function to reinforce the accuracy and robustness of our model. Comparative experiment results on the HRSID dataset show that the proposed GS-YOLO achieves a significant increase in detection precision from 88.2% to 92.7% and the mean Average Precision (mAP) from 90.5% to 94.3%. In addition, the parameters are effectively reduced, achieving a good balance between detection speed and accuracy.

Keywords

• Computer vision
• lightweight network
• SAR ship detection
• YOLOv5s