Research on target detection algorithm based on fusion of LiDAR and camera

A target detection algorithm based on the fusion of LiDAR and camera is proposed for the problems of single sensors in target detection, such as the camera is sensitive to light conditions and difficult to obtain the three-dimensional position of the target, while LiDAR is difficult to accurately determine the target species. Firstly, a point cloud target detection method based on Euclidean clustering is used to divide the point cloud into multiple clusters; secondly, on the basis of the 3D target detection of the point cloud, the fusion rule is derived by comparing the consistency of the detection results of a large number of images with the 3D point cloud, and judging whether it is the same target according to the IOU. Subsequently, experimental validation is carried out, and the results show that the fused target detection is effective in various scenarios, with an error of 5.6% and an accuracy of 94.4%, which meets the needs of multidisciplinary applications.