In order to solve the problems such as insufficient recognition accuracy,low robustness,and slow detec-tion speed in existing methods for recognizing tunnel borehole residues,an algorithm named Cascade Mask Region-Convolutional Neural Network(Cascade Mask R-CNN)is proposed.This algorithm is based on the Cascade Mask R-CNN instance segmentation algorithm and utilizes the advanced ResNeSt network as its backbone(Cascade Mask R-CNN-S)to enhance the feature extraction capability,thereby improving recognition accuracy.Multi-scale training methods and learning rate adjustment strategies are employed to train the network,resulting in an intelligent recogni-tion model that enhances the robustness of the recognition algorithm.The model's performance was compared to tra-ditional algorithms like Cascade Mask R-CNN and Mask R-CNN using mean average precision(mAP)as the evalu-ation metric.The study shows that the improved algorithm achieves an average precision value of 0.415 for bounding boxes(b_mAP(50))and 0.350 for segmentation(s_mAP(50))at an IoU threshold of 0.5.Compared to traditional in-stance segmentation algorithms,the improved algorithm significantly enhances the accuracy of tunnel borehole resi-due recognition,with a length recognition error of only 8.3％.It also demonstrates better robustness and anti-inter-ference capabilities in the complex working environment of tunnels.
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