Research on Object Detection Method Based on Multi-sensor Fusion
In order to improve the perception ability of intelligent vehicles to obstacles in the driving area,a dual mode fusion model of camera image and laser radar point cloud fusion was designed.YOLOv3-tiny is selected as the image object detection algorithm and PointRCNN is selected as the point cloud object detection algorithm.The ROI image ob-tained from point cloud detection is weighted fused with the original image and the object detection is performed on the image containing the obstacle position information after fusion.By comparing with the single-mode object detection model based on image or point cloud,it is found that the better object detection effect is obtained when the appropriate weighted fusion coefficient is selected.On the KITTI dataset,the mAP@.5:.95 of the total category has increased by 3.3%and the AP@.5:.95 of the Cyclist obstacle category has increased by 5.7%.The introduction of lidar point cloud greatly im-proves the detection ability of the pure vision object detection model on small target obstacles.
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