基于深度学习与点云数据的3D检测系统应用研究

Application Research of 3D Detection System Based on Deep Learning and Point Cloud Data

为了解决高低差场景中平坦度高的2D视觉检测乏力的问题,基于深度学习和点云数据处理平台,融合3D点云格式图像和深度学习技术,建立微米精度、在线检测、成本可控的3D检测架构机制。采用相移和光栅投影结构光技术的硬件方案获取3D点云原始数据,基于强大的CPU和GPU处理芯片,对经过被测物体调制的光栅图案进行重新编码,并结合标定参数解算3D点云数据。对2D深度学习模型进行升级开发,可对点云数据进行标注、学习训练和检测,并将3D硬件、3D软件和3D算法进行整合。实验结果表明,所提系统有利于3D缺陷检测系统的落地,为智能3D检测设备奠定算法和软件基础。

In order to solve the problem of weak 2D visual detection with high flatness in high and low difference scenes,based on deep learning and point cloud data processing platform,this research integrates the 3D point cloud format image and deep learning technology to establish a 3D detection architecture mechanism with micron precision,online detection and controllable cost.The hardware scheme of phase shift and grating projection structured light technology is used to obtain the original data of 3D point cloud.Based on the powerful CPU and GPU processing chip,the grating pattern modulated by the measured object is recoded,and the 3D point cloud data is calculated combined with the calibration parameters.The 2D deep learning model is upgraded and developed,which can label,learn,train and detect the point cloud data,and integrate 3D hardware,3D software and 3D algorithms.The experimental results show that the system is conducive to the landing of 3D defect detection system,and lays the algorithm and software foundation for intelligent 3D detection equipment.