Target Detection Algorithm of SAR Ship Image Based on Improved Yolov8n

With regard to ship detection in SAR images, there are problems that the ship scale changes greatly and the background environment is complex in SAR images, which leads to the low accuracy of ship detection. In order to solve the above problems, this paper proposes a target detection algorithm for SAR ship images based on the improved Yolov8n. Firstly, by introducing BiFPN (bi-directional feature pyramid network) into Yolov8n, cross-scale feature fusion is realized, and the model pays more attention to information of different scales. Secondly, LSK (Large Separable Kernel Attention) attention mechanism is added after the SPPF network of the model, which can capture more ship information and reduce the computational load in the complex background environment. Finally, Wise- IoU v2 is used to replace the original CIoU function in Yolov8n to improve the detection accuracy. The improved performance of the model is verified by using public data sets SAR-Ship-Dataset dataset and SSDD dataset. The experimental results show that the improved Yolo v8n ship detection model is better than Yolov8n algorithm model to a great extent. On the SAR-Ship-Dataset dataset, mAP0.5 improves by 0.423% and mAP0.5-0.95 improves by 2.362%; On the SSDD dataset, mAP0.5 improves by 0.551 % and mAP0.5-0.95 improves by 3.147%.