Research On Key Technologies Of Target Detection For Vehicle-mounted 4D Millimeter-wave Radar

In the context of the rapid development of Advanced Driving Assistance System(ADAS),the 4D millimeter wave radar system has attracted wide attention from researchers due to its low cost and unaffected by harsh environments,which is of great significance.The research of object detection technology about the system is also very urgent.Based on the research of 4D millimeter wave radar system principles and key technologies,we collects and produces 4D radar point cloud data sets.The paper analyzes the 4D radar point cloud data,and proposes a set of point cloud object detection schemes suitable for vehicle-mounted 4D millimeter wave radar,which effectively improves the accuracy of the system's environmental perception.The main contents of this article are as follows:According to the characteristics of point cloud distribution,a fusion filtering algorithm is studied.Through the fusion processing of statistical filtering,radius filtering and bilateral filtering algorithm,outliers and noise points mixed with the data are effectively filtered.Aiming at the problem that the DBSCAN clustering algorithm is sensitive to parameters,a solution to adaptively find the optimal DBSCAN parameters is studied.The selected optimal parameters can effectively filter out false points from ground objects and identify point cloud data with similar characteristics,providing a lot of prior knowledge for subsequent work.Next,the paper studies the point cloud object detection network Point RCNN based on deep learning,and combines the point cloud distribution to design an improved network-Threebranch Point RCNN network,which can weaken the influence of the uneven distribution of the point cloud on the detection results.In addition,a sampling strategy based on uncertainty is adopted to balance the effectiveness and diversity of data sampling to obtain more concentrated branches and better detection performance.The detection effects of the two models are compared to verify the effective work of the improved network.Finally,the paper performs experiments and result evaluation on the detection network.The AP of directly inputting the dataset into the Point RCNN network for vehicle target detection is 62.23%,and the AP of directly inputting into the improved network is 65.11%,which can reach 66.89% detected by the proposed scheme in the paper,proving the effectiveness of the detection algorithm.The paper preliminarily solves the problem of the lack of 4D millimeter-wave radar object detection technology,and provides reference for future research.