doi:10.16186/j.cnki.1673-9787.2020100031

Received:2020/10/15

Revised:2021/03/15

Published:2022/07/15

Research on tracking and prediction of rock fissure development based on improved Faster R-CNN algorithm

HUANG Xiaohong1, LI Tiefeng1, LIU Xiangxin2, LI Wei1

1.College of Artificial Intelligence，North China University of Science and Technology，Tangshan 063210，Hebei，China;2.College of Mining Engineering，North China University of Science and Technology，Tangshan  063210，Hebei，China

Abstract: In order to track and predict the development of rock surface cracks so as to avoid the occurrence of some geotechnical disasters and improve the detection rate of rock surface fissures, a method for tracking and predicting rock fissure development based on improved Faster R-CNN algorithm was proposed. Aiming at the characteristics of rock fissures in the infrared thermal image that the shape of the rock fissures were variable, the size difference was large,and there were certain requirements for real-time performance , the deep residual network ResNet50was used as the feature extraction network , and ROIAlign was used to optimize the space between the proposed frame and the rock fissure feature. A feature pyramid was established to fuse multi-scale features to improve the Faster R-CNNalgorithm, and the average infrared radiation temperature-time curve was used to track and predict the development of rock fractures. The infrared features collected in the uniaxial compression experiment of granite were used for experimental testing. The results showed that this method had a good detection effect on the rock fissures in the infrared thermal image. The mAP on the test set reached 88. 81%，and the generalization ability was strong. Compared with other common target detection methods，the detection effect was better. At the same time，combined with the average infrared radiation temperature-time curve in the detection frame，it had a certain effect on the tracking and prediction of the crack development.

Key words:rock fissure;thermal infrared image;average infrared radiation temperature;target detection;improved Faster R-CNN algorithm

基于改进Faster_R-...石裂隙发展方向跟踪预测研究_黄晓红.pdf