Semantic Enhancement and High-order Strong Interaction for Ship Detection in SAR Images
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.
万方数据
维普
