Segmentation-assisted Multi-frame Radar Target Detection Network in Clutter Traffic Scenarios

Target detection in road clutter environment is a challenge for automotive radar. The performance of model-based methods degrades when the prior model is mismatched or the target energy is overwhelmed by the clutter. In contrast, deep learning methods can nonlinearly fit clutter distributions and extract deep features to identify targets from clutter backgrounds. Considering that the spatial-temporal feature in multi-frame data helps distinguish targets from clutter, we use the multi-frame data for detection. This paper proposes a multi-frame detection network for radar moving targets in clutter environment. First, we use transformer as the backbone to fit the large-scale clutter background by extracting the global spatio-temporal feature. Second, we proposed a multi-frame detection head to predict multi-frame bounding boxes in parallel by utilizing the spatio-temporal feature. Third, we proposed a segmentation-assisted refinement module to refine the objectness of bounding boxes, thus further suppressing the false alarms caused by clutter. Through experiments on simulation and measured datasets, the proposed method effectively reduces false alarms while maintaining a high detection probability. In addition, compared with the segmentation-based method, our method distinguishes adjacent targets more robustly.