基于改进RetinaNet的SAR图像目标检测方法

近年来,深度学习方法在合成孔径雷达(SAR)图像目标检测中得到了广泛的应用.船舶出现在近海、港口、岛礁、远洋等各种场景中,同时海洋环境复杂多变,使得船舶目标检测很难排除混乱背景的干扰.对于大纵横比、任意方向、密集分布的目标,精确定位变得更加复杂.本文基于深度学习的方法提出用于SAR图像目标检测的改进RetinaNet模...

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.