A Coarse-to-Fine Approach for Rock Bolt Detection From 3D Point Clouds

Rock bolts have been widely used to enhance the structural stability of underground infrastructures. Careful tracking of rock bolt positions is highly significant since it assists with operational success of ground support and has applications to predictive maintenance practices. This paper presents a practical algorithm, CFBolt, to detect rock bolts from a 3D laser scanned point cloud. Considering that rock bolts are relatively tiny objects, CFBolt follows a two-step coarse-to-fine strategy. It first computes a single-scale proportion of variance (POV) for each point as the local point descriptor and filters out near 95% not-bolt points with a simple but effective classifier, Linear Discriminant Analysis (LDA), which allows for the pruned point cloud to be then used as a compatible input to a deep neural network, designed and trained to precisely detect rock bolts from the pruned point cloud. CFBolt was tested for detecting rock bolts from LiDAR scan data collected from Sydney's civil tunnelling project site. The entire dataset contains more than 160 million points. The obtained scores of Intersection over Union (IoU) and precision for individual bolt points were 89.33% and 92.04%, respectively. For rock bolt objects, the precision and recall were 98.34% and 98.73%, respectively. The detection quality of CFBolt is superior to the state-of-the-art 3D object detection algorithms and the newest rock bolt detection algorithm, demonstrating the robustness and effectiveness of CFBolt.

Automated summary

The CFBolt algorithm utilizes a two-step strategy, utilizing Linear Discriminant Analysis and a deep neural network, to successfully detect rock bolts in LiDAR scan data from Sydney's tunneling project site, demonstrating high detection quality.