Traffic vehicle detection by fusion of millimeter wave radar and camera

Abstract

Aiming at the defects of poor identification effect and prone to be disturbed by weather and illumination changes in vehicle detection using a single sensor, a multi-sensor fusion sensing system based on millimeter wave radar and camera was designed in this paper. Firstly, the spatial and temporal coordinates of millimeter wave radar and camera are unified through coordinate transformation and time alignment. Then, YOLOV5 deep neural network model is used to realize target detection of camera data, including cars, trucks and buses. Finally, data fusion is realized according to the detection results of the two sensors. Through field experiments, the vehicle detection accuracy reaches 95.3%. The results show that the proposed system overcomes the deficiency of single sensor in target detection, which can improve the reliability and effectiveness of vehicle detection.