TY - GEN
T1 - Instance-Segmentation-Based Dense On-Site Rock Fragment Recognition During Real-World Tunnel Excavation
AU - Yang, Xu
AU - Weidong, Qiao
AU - Hui, Li
N1 - Publisher Copyright:
© 2021 Structural Health Monitoring 2021: Enabling Next-Generation SHM for Cyber-Physical Systems - Proceedings of the 13th International Workshop on Structural Health Monitoring, IWSHM 2021. All rights reserved.
PY - 2021
Y1 - 2021
N2 - Timely recognition of rock fragments and their morphological sizes can help adjust excavation parameters during tunnel boring machine (TBM) tunneling. Traditional manual inspection highly relies on experiences and subjective judgments of human operators and conducting sieving tests is not real-time and energy-consuming. Rock fragments in real-world images are often observed against a dark background, distributed with high size diversity, complicatedly distributed, and blocked by each other. To solve these problems, this study proposes a novel instance segmentation-based method for on-site rock fragments recognition. The proposed instance segmentation model includes two subnetworks: object detection and semantic segmentation. The results show that 88% of rock fragments can be recognized, and the average recall and average IoU values reach 0.85 and 0.75 on the 15 test images, respectively. Besides, both small and large rock fragments can be recognized well. The predicted size distributions of the major and minor axis lengths of the rock fragments fit well with the ground-truth ones statistically. In conclusion, this study can provide both visual recognition and statistical results for the size distribution of on-site rock fragments.
AB - Timely recognition of rock fragments and their morphological sizes can help adjust excavation parameters during tunnel boring machine (TBM) tunneling. Traditional manual inspection highly relies on experiences and subjective judgments of human operators and conducting sieving tests is not real-time and energy-consuming. Rock fragments in real-world images are often observed against a dark background, distributed with high size diversity, complicatedly distributed, and blocked by each other. To solve these problems, this study proposes a novel instance segmentation-based method for on-site rock fragments recognition. The proposed instance segmentation model includes two subnetworks: object detection and semantic segmentation. The results show that 88% of rock fragments can be recognized, and the average recall and average IoU values reach 0.85 and 0.75 on the 15 test images, respectively. Besides, both small and large rock fragments can be recognized well. The predicted size distributions of the major and minor axis lengths of the rock fragments fit well with the ground-truth ones statistically. In conclusion, this study can provide both visual recognition and statistical results for the size distribution of on-site rock fragments.
UR - https://www.scopus.com/pages/publications/85139207508
M3 - 会议稿件
AN - SCOPUS:85139207508
T3 - Structural Health Monitoring 2021: Enabling Next-Generation SHM for Cyber-Physical Systems - Proceedings of the 13th International Workshop on Structural Health Monitoring, IWSHM 2021
SP - 721
EP - 728
BT - Structural Health Monitoring 2021
A2 - Farhangdoust, Saman
A2 - Guemes, Alfredo
A2 - Chang, Fu-Kuo
PB - DEStech Publications Inc.
T2 - 13th International Workshop on Structural Health Monitoring: Enabling Next-Generation SHM for Cyber-Physical Systems, IWSHM 2021
Y2 - 15 March 2022 through 17 March 2022
ER -