Abstract
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 be significantly reduced. In this paper, the range-azimuth-frame information obtained by scanning radar is converted into plain position indicator (PPI) images, and a novel Radar-PPInet is proposed and used for marine target detection. The model includes CSPDarknet53, SPP, PANet, power non-maximum suppression (P-NMS), and multi-frame fusion section. 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. P-NMS can effectively improve the problem of missed detection of multi-targets. Moreover, the false alarms caused by strong sea clutter are reduced by the multi-frame fusion, which is also a benefit for weak target detection. The verification using the X-band navigation radar PPI image dataset shows that compared with the traditional cell-average constant false alarm rate detector (CA-CFAR) and the two-stage Faster R-CNN algorithm, the proposed method significantly improved the detection probability by 15% and 10% under certain false alarm probability conditions, which is more suitable for various environment and target characteristics. Moreover, the computational burden is discussed showing that the Radar-PPInet detection model is significantly lower than the Faster R-CNN in terms of parameters and calculations.
Highlights
Radar is one of the main sensors for marine target detection, which can detect targets by emitting electromagnetic waves without being restricted by day and night, and can penetrate clouds, rain, and fog [1]
Radar plain position indicator (PPI) Image Target Detection Based on the Two-Stage Detection Algorithm sion network.Faster shared convolutional neural network (CNN) can extract target features and input the extracted feature maps into the region proposal network (RPN)
Crop PPI images, state navigation radar, which is mainly used in ship navigation and coastal surveillance scenarios
Summary
Radar is one of the main sensors for marine target detection, which can detect targets by emitting electromagnetic waves without being restricted by day and night, and can penetrate clouds, rain, and fog [1]. The sea environment is complex and the marine targets are diverse, so the target’s returns are sometimes weak, which makes radar detection rather difficult [2,3]. The environment is complex and changeable, and it is difficult to describe the clutter characteristics using specific distribution models [5], e.g., Rayleigh distribution, log-normal distribution, or K distribution. Traditional statistical detection methods need to assume models, such as the distribution types and distribution characteristics of the background [4,5,6]. Each bounding box contains the prediction box (x, y, w, h) and confidence scores. After determining the initial value of the model weight and the input image, the 8 of 20 position of the bounding box is determined by model calculation, and the GT is used in the training process to adjust the predicted position of the bounding box
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