Lightweight Vehicle Detection Algorithm Based on Adaptive Grid Clustering
A lightweight vehicle detection algorithm based on an adaptive grid clustering was proposed.Radius filtering and the least square method were used to remove noise points from the original point cloud and perform ground fitting processing.Maximum and minimum grid transformation and an adap-tive grid clustering algorithm were employed to generate several clustering targets.A multi-layer per-ceptron(MLP)network was utilized to perform binary classification of the clustering targets.Training and algorithm validation experiments were conducted on the KITTI datasets.The experimental results show that the algorithm has environmental adaptability in different scenes.Compared with the other 3D detection algorithms,the proposed algorithm improves the average accuracy of vehicle recognition by 7.95%.
万方数据
维普
