Marine Targets Detection for Scanning Radar Images Based on Radar- YOLONet

Traditional radar target detection algorithms are mostly based on statistical theory. They have weak generalization capabilities for complex sea clutter environments and diverse target characteristics, and their detection performance would significantly be reduced. In this paper, the range-azimuth information obtained by scanning radar is converted into PPI image, and a Radar-YOLONet is proposed and used for radar image target detection. The model includes CSPDarknet53, SPP, PANet and Power Non-Maximum Suppression layers. The prediction frame coordinates, target category and corresponding confidence are directly given through the feature extraction network. The network structure strengthens the receptive field and attention distribution structure, and further improves the efficiency of network training. The verification using the constructed X-band navigation radar PPI image dataset shows that compared with the traditional constant false alarm rate detector and the two-stage Faster-RCNN algorithm, the proposed method has significantly improved detection performance under certain false alarm probability conditions. It is more suitable for various environment and target characteristics.