An enhanced object detection network for ship target detection in SAR images.

Deep learning techniques have made significant advancements in computer vision. The YOLO algorithm, a representative single-stage detection approach, has demonstrated remarkable results in detecting ship targets in SAR images. We introduce an enhanced ship target detection model for SAR images, utilizing the improved YOLOv7 object detection network. We incorporate the coordinate attention mechanism into the network to enable automatic detection and diagnosis of ship targets within SAR images. To enhance the robustness and positioning accuracy of the detection network, we replace the CIoU regression loss in YOLOv7 with the SIoU loss, reducing the complexity of the loss function. Additionally, we integrate the rotating target detection technology into the network to mitigate the impact of target overlap on detection results. Comprehensive experiments conducted on the Capella Space synthetic aperture radar datasets validate that the proposed methodology achieves superior performance in multiple evaluation metrics, including precision, recall, and mean average precision. [ABSTRACT FROM AUTHOR]