基于改进级联式BP神经网络的巷道点云分类  

Classification of tunnel point clouds based on improved cascaded BP neural network

作　　者：丁鹏辉 李志远[3] 刘艺[3] 王政辉 DING Penghui;LI Zhiyuan;LIU Yi;WANG Zhenghui(Qingdao Surveying and Mapping Institute,Qingdao 266033,China;National-local Joint Engineering Research Center of Integration and Application of Marine terrestrial Geographical Information,Qingdao 266000,China;College of Geodesy and Geomatics,Shandong University of Science and Technology,Qingdao 266590,China)

机构地区：[1]青岛市勘察测绘研究院,山东青岛266033 [2]海陆地理信息集成与应用国家地方联合工程研究中心,山东青岛266000 [3]山东科技大学测绘与空间信息学院,山东青岛266590

出　　处：《测绘通报》2024年第11期172-176,共5页Bulletin of Surveying and Mapping

基　　金：山东省自然科学基金(ZR2023MD027);“十四五”国家重点研发计划(2022YFB3903501)。

摘　　要：巷道点云数据的高效分类对于地下交通和矿山开采的安全监测与三维重建具有至关重要的意义,可以推动点云数据的充分挖掘与利用。本文针对现有巷道点云分类方法存在的噪声敏感、处理效率低、易出现过拟合等问题,提出了一种自适应调参的早停机制优化的级联式反向传播(CBP)神经网络分类方法。该方法首先通过Trimble RealWorks软件分离巷道与巷道地面点云;然后利用球形邻域空间和协方差矩阵特征值提取局部几何特征,构建特征向量;最后通过改进的CBP网络实现巷道内部照明设备、指示牌和多类管线的分级分类,提高了分类效率和精度。试验结果表明,改进后的级联式BP神经网络在巷道点云分类方面具有较高的准确性和可靠性,提高了数据处理的效率,为巷道维护、改造和安全管理提供了数据支撑。Efficient classification of tunnel point cloud data is crucial for safety monitoring and 3D reconstruction in underground transportation and mining operations,as it facilitates the comprehensive exploration and utilization of point cloud data.This study addresses issues in existing tunnel point cloud classification methods,such as noise sensitivity,low processing efficiency,and susceptibility to overfitting,by proposing a cascaded backpropagation(CBP)neural network classification method optimized with an early stopping mechanism and adaptive parameter tuning.Firstly,the Trimble RealWorks software is used to separate tunnel and ground point clouds.Then,local geometric features are extracted using spherical neighborhood space and covariance matrix eigenvalues to construct feature vectors.Finally,an improved CBP network is employed to hierarchically classify internal tunnel lighting equipment,signage,and various pipelines,thereby enhancing classification efficiency and accuracy.Experimental results demonstrate that the improved CBP neural network achieves high accuracy and reliability in tunnel point cloud classification,significantly improving data processing efficiency and providing data support for tunnel maintenance,renovation,and safety management.

关 键 词：点云 巷道 级联式BP神经网络 特征提取 分类 

分 类 号：P237[天文地球—摄影测量与遥感]