SAR Target Detection Network Based on Saliency-Combined Single Shot Multi Box Detector

Abstract

The Single Shot multi-box Detector (SSD) has been successfully applied in synthetic aperture radar (SAR) target detection. Besides, the saliency information in the saliency map has the ability that strengthening the target of interest and suppressing the clutter. It will help to improve the capability of scene understanding. According to the above, a novel SAR target detection network based on saliency-combined SSD is proposed. The proposed method includes two backbone sub-networks, one taking the SAR images as input for extracting the features, and the saliency map obtained from traditional saliency method is used as the input of the other sub-network to obtain the refined saliency information. Through the fusion module is used in multiple scales to integrate the saliency information and the network feature. Finally, we can get the detection results by the convolutional predictors on the multi-scale integrated feature maps. In addition, we apply the dense connection structure in the two sub-networks to utilize context information. The experimental results based on the miniSAR real data show that the proposed method can achieve a good detection performance.