基于改进YOLOv8的SAR图像舰船目标检测算法研究OA

Since the synthetic aperture radar(SAR)image ship target detection algorithm is difficult to balance between accuracy,computational efficiency and model complexity,an SAR image ship target detection algorithm based on improved YOLOv8 is proposed.Firstly,a P2 detection head is added to the part of the Head to improve the detection ability of small-scale targets.Secondly,the enhanced multi-scale channel perception(EMSCP)structure is introduced into the C2f module to enhance the feature expression ability and optimize the multi-scale object detection effect.The convolutional block attention module(CBAM)is added in front of the detection head to improve the model's attention to key features.In addition,Ghost lightweight convolution is used to reduce the computation burden and improve the inference speed of the model.The experimental results on HRSID(high-resolution SAR images dataset for ship detection)show that in comparison with the original YOLOv8,the mean average precision(mAP)of ship target detection of the improved algorithm is improved by 2.8%,its recall rate is increased by 4.2%,its detection speed FPS is increased by 27.1 f/s,and its computation burden GFLOPs is reduced by 25.17%.In comparison with RCSA-YOLO,although the computation burden of the proposed algorithm is slightly increased,its mAP is 4.7%higher,and its accuracy is also higher than that of RCSA-YOLO.In comparison with the other algorithms,the proposed algorithm reduces the number of model parameters and computation burden greatly and improves the detection efficiency while ensuring high detection accuracy.Experimental results show that the improved YOLOv8 algorithm achieves a balance between detection accuracy,detection efficiency and model complexity,so it has high practical value for SAR small-scale ship detection in complex background,and can provide support for real-time applications such as maritime surveillance and port security.