A New Approach for Discontinuity Extraction Based on an Improved Naive Bayes Classifier
Source abstract
Abstract
An increasing number of methods are being used to extract rock discontinuities from 3D point cloud data of rock surfaces. In this paper, a new method for automatic extraction of rock discontinuity based on an improved Naive Bayes classifier is proposed. The method first uses principal component analysis to find the normal vectors of the points, and then generates a certain number of random point sets around the selected training points for training the classifier. The trained, improved Naive Bayes classifier is based on point normal vectors and is able to automatically remove noise points due to various reasons in conjunction with the knee point algorithm, realizing high-precision extraction of the discontinuity sets. Subsequently, the individual discontinuities are segmented using a hierarchical density-based spatial clustering method with noise application. Finally, the PCA algorithm is used to complete the orientation by plane fitting the individual discontinuities. The method was applied in two cases, Kingston and Colorado, and the reliability and advantages of the new method were verified by comparing the results with those of previous research, and the discussion and analysis determined the optimal values of the relevant parameters in the algorithm.
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Research context
OpenAlex topics: Image Processing and 3D Reconstruction · Anomaly Detection Techniques and Applications · Image and Object Detection Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
National Engineering Laboratory for High Speed Railway Construction, Changsha 410075, China
Hunan Key Laboratory of Nonferrous Resources and Geological Hazards Exploration, Changsha 410083, China
Funding
Science and Technology Research and Development Project of China Railway Co., Ltd. · National Natural Science Foundation of China · Natural Resources Science and Technology Project of Hunan Province · Research Foundation of the Department of Natural Resources of Hunan Province
References (53)
Show source-provided references
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