Instance-Segmentation-Based Dense On-Site Rock Fragment Recognition During Real-World Tunnel Excavation

Abstract

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.