HIGH PRECISION AND LIGHT-WEIGHT NETWORK FOR LOW RESOLUTION SAR IMAGE DETECTION

Target areas of low resolution SAR images usually have blurred edge and large background noise, so most common object detection methods based on deep learning have obvious errors in this occasion. In this paper, we propose a high precision and lightweight network for low resolution SAR image detection. We take generalized distribution to model bounding box in training and predicting to better indicate target area boundaries in low resolution SAR images, improving detection accuracy. Moreover, we introduce "teacher-student" knowledge distilling method, which greatly reduces model parameters and further enhances the detection accuracy. Compared with conventional deep learning networks(Faster R-CNN, SSD, CenterNet, FCOS and YOLOv3) on low resolution SAR images, the results show that our method has not only the best performance in target area extractionn, but rather light weight.