Lightweight Millimeter Wave Radar and Camera Fusion Detection Algorithm Based on YOLOX
In order to meet the needs of all-weather,efficient,and accurate perception in traffic systems,a lightweight millimeter-wave radar and camera fusion detection algorithm based on YOLOX is studied;The study mainly includes two aspects:the fusion of heterogeneous sensor information and model lightweight;The fusion of heterogeneous sensor information primarily addresses the in-sufficient auxiliary ability of the radar information to the visual information,and the radar spatial attention module is designed to ef-fectively extract the radar spatial features,thus help the vision to learn the robust feature expression in low visibility scenarios;Train-ing and testing are carried out on the self-made datasets and NuScenes datasets,the proposed RV-YOLOX algorithm increases the AP index by approximately 3～4 times,compared with the YOLOX algorithm,indicating an enhancement in all-weather detection capabil-ity;The lightweight model addresses the restrictions of algorithm deployment on terminal computing devices,the structural reparame-terization is used to optimize the RV-YOLOX,the lightweight RV-YOLOX improves inference speed while achieving detection accura-cy comparable to the RV-YOLOX.
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