Ship Detection in SAR Images Based on Improved RetinaNet

In recent years， deep learning method has been widely used in target detection in synthetic aperture radar （SAR） images. Ships appear in various scenes such as nearshore， port， island and reef， ocean. The complex and changeable marine environment also makes it difficult for ship detection to eliminate the interference of chaotic background. For targets with large aspect ratio， arbitrary direction and dense distribution， accurate positioning becomes more difficult. In this paper， an improved RetinaNet model for target detection in SAR images was proposed based on deep learning method. The depth residual network was used to obtain image features independently. The rotate anchor based on circular smooth label （CSL） was used to achieve accurate positioning. The attention mechanism was added to the classification and detection network to enhance the network feature extraction ability. Experimental results on SSDD dataset showed that the detection accuracy of the proposed method reached 88.63%， which was 8.74% higher than that of the conventional RetinaNet model， showing a good detection performance.