Multi-class object detection in tunnels from 3D point clouds: An auto-optimized lazy learning approach. (April 2022) - Wang, Kunyu; Zhang, Zhaoxiang; Wu, Xianguo; Zhang, Limao
Abstract: This research develops an auto-optimized lazy learning approach named BOKNN (Bayesian optimized K nearest neighbor) method to detect seepage and multi-classify various objects (e.g., segment, pipe, track, support, and cable) in operating tunnels from 3D point clouds. Firstly, the 3D laser scanning is employed to acquire raw point cloud data, and the equidistant pooling for down-sampling is conducted to improve class imbalance issues and enhance the efficiency. Then, the K-nearest neighbor (KNN) model is built on the trimmed dataset, where the Bayesian optimization is performed to obtain the optimal combination of hyper-parameters in the KNN model. A realistic cross-river tunnel section in China is used as a case study to demonstrate the applicability and effectiveness of the developed approach. Results indicate that (1) The established BOKNN model displays a high performance in multi-class detection, together with a total accuracy of 0.935, a macro F1 score of 0.896, and a weighted F1 score of 0.939. (2) It performs well even in minor class detection, and the detection of seepage is conservative, where only 4.1% of seepage points are misclassified as non-seepage points. (3) It displays better detection performance than the other representative machine learning models (i.e, Adaboost, Support Vector Machine, and Naive Bayes). The developed approach is nonparametric and training-free, which can be used as a decision tool to substitute the present manual detection and improve the detection efficiency.