ng SAR images for ship detection,it is inevitable to be affected by speckle noise,and nearshore ship detection is easily overwhelmed by complex background signals.A ship detection algorithm RBox-YOLO based on edge feature fusion network is proposed.Using YOLOv8 as the baseline network,the edge of Canny operator is optimized to enhance the contour edges in the image,forming a more complete object boundary.An FDN module based upon coordinate attention mechanism is introduced to fuse denoised images to improve the ability of capturing key information in complex background.The CAU module,which combines bilinear interpolation method with attention mechanism,reduces the detail feature loss caused by upsampling.In addition,a loss function on the basis of rotating frame is used to enhance the ship detection effect under complex background.The experimental results show that RBox-YOLO not only maintains the real-time detection speed of YOLOv8 algorithm,but also improves the average accuracy by 8 percentage points.It is preliminarily concluded that RBox-YOLO algorithm has good detection performance and high application value.