Vehicle Detection Based On Lidar Point Cloud Data

With the continuous progress of computer vision technology,the era of automatic driving is also coming to us step by step.As an important part of automatic driving,the perception module provides important information for the decision-making module and planning module of automatic driving.In recent years,research on deep learning has mushroomed,the use of convolutional neural network for point cloud data target detection has gradually become the mainstream.Based on the 32 line lidar data,this paper studies the vehicle detection based on the point cloud data of deep learning.The algorithms studied and designed in this paper include point cloud voxelization,network layer structure design,anchor box design,coding and decoding technology,coordinate transformation and NMS,etc.the main results of this paper are as follows:(1)Based on the innovation of FAF,it is changed into a single task end-to-end network for target detection.FAF is a multi task network based on LIDAR point cloud data for detection,classification and tracking,so the network needs to input multi frames of point cloud data for forward propagation every time,while this paper is a single task detection network,each input is a single frame point cloud data.Vgg16 is used in the network,but the last block unit is removed,and the convolution number of each feature layer is changed to half of the original.(2)The initialization method used in this article is Kaiming initialization,not Xavier initialization.Xavier initialization method is specially designed for linear activation function,but if the deep neural network uses linear activation function,it will not be able to achieve complex functions,so it uses nonlinear activation function such as Relu.The activation function used in this paper is leakyrelu,Kaiming initialization is more friendly to relu function and its variants.(3)IOU calculation of NMS is rectangles with any rotation angle.The bounding box drawn by traditional target detection tasks is usually a rectangular box parallel to the coordinate axis,while the bounding box drawn in this paper is a rectangular box not parallel to the coordinate axis,with any rotation angle,so the bounding box can more fit the actual object.The calculation of IOU is a rectangle with any rotation angle.