CFAR-Guided Dual-Stream Single-Shot Multibox Detector for Vehicle Detection in SAR Images

Recently, deep neural network has achieved enormous success in target detection but how to better apply these methods to synthetic aperture radar (SAR) images is still a challenge due to the following two reasons: the characteristics of SAR images are not utilized sufficiently; SAR images contain a smaller number of targets, and thus detectors face more serious foreground-background class imbalance problem during training. In this letter, we propose a constant false alarm rate (CFAR)-guided dual-stream single-shot multibox detector (SSD) to solve the above problems. First, based on the proposed dual subnetworks and the interactive channel-spatial attention fusion (ICSAF) module, we effectively utilize the strong scattering characteristic of the SAR target to improve the feature extraction ability. Then we integrate the CFAR indicative map into focal loss to alleviate the foreground-background class imbalance problem. In addition, a non-maximum suppression method based on area ratio (AR-NMS) is proposed for the problem caused by dividing large scene SAR images into blocks. The whole framework can realize end-to-end joint training, and the experimental results on miniSAR real data demonstrate the effectiveness of the proposed method.