3D object detection based on point cloud in automatic driving scene

In many real-time applications such as autonomous driving and robotics, 3D object detection algorithms represented by PointPillars have great potential to design fast and reliable 3D object detection algorithms by using point cloud columns (Pillars) to represent point clouds. However, this kind of algorithm still has some shortcomings, such as poor detection results for some small objects or distant objects and the existence of wrong detection, missing detection and other problems. In order to solve these problems, we design a three-branch extended convolutional network in the 3D object detection algorithm, which can alleviate the insensitivity of the original network to targets of different sizes, especially small targets. Then, we design an improved hybrid attention mechanism network in 3D object detection algorithm to solve the problem of missing detection and error detection in long-distance vehicle detection. From the experimental verification of KITTI dataset, we draw the following conclusion: Our network has great advantages compared with PointPillars, especially the big improvement in the mAP(mean Average Precision) of vehicle detection and pedestrian and rider detection, in the case that the detection speed is basically equal to PointPillars.