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.

针对带有俯仰运动装置的二维激光雷达转换的扫描系统，提出了一种基于级联深度神经网络的三维激光扫描策略。该策略旨在检测和监控移动目标。结合设备特点，该策略首先提出了一个级联深度神经网络，输入二维点云、彩色图像和俯仰角。输出是目标距离和速度分类。并通过使用焦点损失和均匀分布对网络的交叉熵损失函数进行修改，以提高识别精度。然后提出了一个俯仰范围和速度模型来确定俯仰运动参数。最后通过积分分离速度PID实现自适应扫描。实验结果表明，改进后的网络目标检测框的准确率，距离和速度分类分别为 90.17%、96.87% 和 96.97%。改进后的PID平均速度误差为0.4239°/s，平均策略执行时间为0.1521s。距离和速度模型可以有效减少无用信息的收集和目标点云的变形。综上所述，整体扫描策略的实验表明，它可以在保证目标捕获的同时提高目标点云的完整性和密度。