Abstract:

Aiming at the defects of poor identification effects and prone to be disturbed when using traditional single sensor in vehicle detection， a vehicle detection method based on the fusion of millimeter wave radar and machine vision is propose in this paper. Firstly， the radar data is processed by using hierarchical clustering algorithm with invalid targets filtered out， and the improved YOLO v2 algorithm is adopted to reduce the missed detection rate and increase the detection speed. Then， the intersection?over?union （IoU） of target detection and the global nearest neighbor data association algorithm are utilized to achieve multi?sensor data fusion. Finally， the extended Kalman filter algorithm is employed for target tracking， with the final result obtained. The results of real vehicle test show that the results of vehicle identification with the method proposed is better than that with single sensor， and has good recognition effects under various road conditions.

Key words: vehicle detection, millimeter wave radar, YOLO algorithm, sensor fusion, multi?target tracking