Computer-Vision-Based Real-Time Rock Fragment Recognition During Tunnel Excavation
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Bibliographic Details
| Author(s): |
Xu Yang
(Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters of the Ministry of Industry and Information Technology, Harbin Institute of Technology, Harbin 150090, China)
Li Hui (Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters of the Ministry of Industry and Information Technology, Harbin Institute of Technology, Harbin 150090, China) Weidong Qiao (School of Civil Engineering, Harbin Institute of Technology, Harbin, 150090, China) |
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| Medium: | conference paper | ||||
| Language(s): | English | ||||
| Conference: | IABSE Congress: Bridges and Structures: Connection, Integration and Harmonisation, Nanjing, People's Republic of China, 21-23 September 2022 | ||||
| Published in: | IABSE Congress Nanjing 2022 | ||||
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| Page(s): | 1240-1247 | ||||
| Total no. of pages: | 8 | ||||
| DOI: | 10.2749/nanjing.2022.1240 | ||||
| Abstract: |
Timely recognition of rock fragments can help predict the deformation of the tunnel during tunnel boring machine (TBM) tunneling. Traditional manual inspection highly relies on subjective judgments of operators and conducting sieving tests is not real-time. Rock fragments in the real- world are often observed against a dark background, distributed with high size diversity, complicatedly distributed, and blocked by each other. This study proposes a computer vision-based method for on-site rock fragments recognition. The proposed method consists of an image pre- processing module, an instance segmentation model, and a post-processing module. The results show that the pixel-level rock fragment recognition takes 0.15s for processing a 512×512 patch on average and 88% of rock fragments can be recognized. The predicted size distributions of the major and minor axis lengths of the rock fragments fit well with the ground-truth ones statistically. |
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| Keywords: |
computer vision tunnel boring machine tunnelling rock fragment recognition instance segmentation
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| Copyright: | © 2022 International Association for Bridge and Structural Engineering (IABSE) | ||||
| License: | This creative work is copyrighted material and may not be used without explicit approval by the author and/or copyright owner. |
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