Research on Vehicle Detection Algorithms Based on LIDAR
In the complex traffic environment,aiming at the problem of the application of lidar to unmanned vehicle obstacle de-tection,a method of vehicle detection in front based on neural network was proposed.Firstly,the original point cloud is segmented by a straight-through filtering algorithm.Secondly,an end-to-end single-stage detection deep neural network is proposed.In this network,the structure of RetinaNet is optimized by using dilated convolution blocks to enhance the accuracy and robustness of the network against vehicles.Finally,training and testing experiments are carried out on the KITTI dataset.The results show that the number of point clouds is greatly reduced after filtering,and the detection range is marked more accurately.By comparing the processing results of different detection algorithms in the test KITTI data set,it is concluded that the proposed method has faster detection speed and higher application potential on the basis of improved accuracy.
NETL
NSTL
万方数据
维普
