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Computer-Vision-Based Real-Time Rock Fragment Recognition During Tunnel Excavation

 Computer-Vision-Based Real-Time Rock Fragment Recognition During Tunnel Excavation
Autor(en): , ,
Beitrag für IABSE Congress: Bridges and Structures: Connection, Integration and Harmonisation, Nanjing, People's Republic of China, 21-23 September 2022, veröffentlicht in , S. 1240-1247
DOI: 10.2749/nanjing.2022.1240
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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...
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Bibliografische Angaben

Autor(en): (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)
(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)
(School of Civil Engineering, Harbin Institute of Technology, Harbin, 150090, China)
Medium: Tagungsbeitrag
Sprache(n): Englisch
Tagung: IABSE Congress: Bridges and Structures: Connection, Integration and Harmonisation, Nanjing, People's Republic of China, 21-23 September 2022
Veröffentlicht in:
Seite(n): 1240-1247 Anzahl der Seiten (im PDF): 8
Seite(n): 1240-1247
Anzahl der Seiten (im PDF): 8
DOI: 10.2749/nanjing.2022.1240
Abstrakt:

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.

Copyright: © 2022 International Association for Bridge and Structural Engineering (IABSE)
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