The background information in synthetic aperture radar(SAR)images is complex,and the edges of ship targets are often blurred,making it difficult to detect small-scale ship targets that are prone to be missed.To address these issues,this paper proposes a SAR ship detection method that combines semantic enhancement and high-order strong interactions.By using partial convolution and asymmetric convolution,a partially asymmetric convolutional aggregation network is constructed.This network reduces computational complexity and lightweight the backbone network,while better capturing multi-scale ship features.In addition,a dual-path routing attention mechanism is introduced in the upsampling part to enhance the utilization of contextual information in the image.Feature extraction in a recursive manner can better solve the problem of information interaction in the region,realize high-order interactive modeling between different levels of features,and improve model detection capabilities.Experimental results on the publicly available HRSID remote sensing dataset demonstrate that the improved method achieves a detection accuracy of 91.23％,which is a 5.13％improvement over the original model.The precision and recall rates are improved by 2.41％and 7.16％,respectively.Compared to mainstream algorithms,the proposed method achieves better detection performance.
维普
万方数据