3D laser scanning strategy based on cascaded deep neural network  

作　　者：Xiao-bin Xu Ming-hui Zhao Jian Yang Yi-yang Xiong Feng-lin Pang Zhi-ying Tan Min-zhou Luo 

机构地区：[1]College of Mechanical&Electrical Engineering,Hohai University,Changzhou,213022,China [2]Jiangsu Key Laboratory of Special Robot Technology,Hohai University,Changzhou,213022,China [3]College of Mechanical Engineering,Yangzhou University,Yangzhou,225127,China

出　　处：《Defence Technology（防务技术）》2022年第9期1727-1739,共13页Defence Technology

基　　金：funded by National Natural Science Foundation of China(Grant No. 51805146);the Fundamental Research Funds for the Central Universities (Grant No. B200202221);Jiangsu Key R&D Program (Grant Nos. BE2018004-1, BE2018004);College Students’ Innovative Entrepreneurial Training Plan Program (Grant No. 2020102941513)。

摘　　要：A 3D laser scanning strategy based on cascaded deep neural network is proposed for the scanning system converted from 2D Lidar with a pitching motion device. The strategy is aimed at moving target detection and monitoring. Combining the device characteristics, the strategy first proposes a cascaded deep neural network, which inputs 2D point cloud, color image and pitching angle. The outputs are target distance and speed classification. And the cross-entropy loss function of network is modified by using focal loss and uniform distribution to improve the recognition accuracy. Then a pitching range and speed model are proposed to determine pitching motion parameters. Finally, the adaptive scanning is realized by integral separate speed PID. The experimental results show that the accuracies of the improved network target detection box, distance and speed classification are 90.17%, 96.87% and 96.97%, respectively. The average speed error of the improved PID is 0.4239°/s, and the average strategy execution time is 0.1521 s.The range and speed model can effectively reduce the collection of useless information and the deformation of the target point cloud. Conclusively, the experimental of overall scanning strategy show that it can improve target point cloud integrity and density while ensuring the capture of target.

关 键 词：Scanning strategy Cascaded deep neural network Improved cross entropy loss function Pitching range and speed model Integral separate speed PID 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TN958.98[自动化与计算机技术—控制科学与工程]