Deep Instance Segmentation with Automotive Radar Detection Points

Automotive radar provides reliable environmental perception in all-weather conditions with affordable cost, but it hardly supplies semantic and geometry information due to the sparsity of radar detection points. With the developmentof automotive radar technologies in recent years, instance segmentation becomes possible by using automotive radar. Its data contain contexts such as radar cross section and micro-Doppler effects, and sometimes can provide detection when the field of view is obscured. The outcome from instance segmentationcould be potentially used as the input of trackers for tracking targets. The existing methods often utilize a clustering based classification framework, which fits the need of realtime processing but has limited performance due to minimuminformation provided by sparse radar detection points. In this paper, we propose an efficient method based on clustering of estimated semantic information to achieve instance segmentation for the sparse radar detection points. In addition, we show that the performance of the proposed approach can be furtherenhanced by incorporating the visual multi-layer perceptron. The effectiveness of the proposed method is verified by experimental results on the popular RadarScenes dataset, achieving 89.53%mCov and 86.97% mAP0.5, which is the best comparing to other approaches in the literature. More significantly, the proposed algorithm consumes memory around 1MB, and the inferencetime is less than 40ms. These two criteria ensure the practicality of the proposed method in real-world systems.