Intelligent Recognition Method of Tunnel Face Joints and Fissures Using Convolutional Neural Network

To address the issues of insufficient recognition accuracy, low robustness, and slow detection speed in existing tunnel face joint and fissure recognition methods, this paper proposes a novel algorithm called mask-region convolutional neural network-EfficientNet (Mask R-CNN-E) based on the Mask R-CNN instance segmentation algorithm for tunnel face joint and fissure recognition. This algorithm incorporates the advanced EfficientNet as the backbone network to enhance the feature extraction capability of Mask R-CNN, thereby significantly improving recognition accuracy. EfficientNet employs a compound scaling method to effectively balance network depth, width, and resolution, achieving an optimal tradeoff between computational efficiency and accuracy. During the model training process, multiscale training and poly-learning rate adjustment strategies were adopted to enhance the robustness of the algorithm. The performance of the algorithm was evaluated using the mean average precision (Am) metric, and comparative experiments were conducted using the traditional Mask R-CNN algorithm. In addition, a skeleton algorithm was employed to refine the joint and fissure mask outputs of the model to obtain more precise quantitative information on joints and fissures. The results show that the improved algorithm achieved a bounding box mean average precision (b_Am) of 0. 656 and a segmentation mean average precision (s-Am) of 0. 436, with both significantly higher than those of the traditional method, indicating superior recognition accuracy. The improved Mask R-CNN-E algorithm significantly enhances tunnel face joint and fissure recognition, exhibiting stronger robustness and anti-interference capabilities in complex tunnel environments. In terms of joint and fissure length measurements, the algorithmic error was controlled within the range of 1. 5%-9. 8%, which satisfies engineering requirements. This method not only offers high theoretical accuracy and robustness but also provides more reliable support in practical applications, which is crucial for improving the safety and efficiency of tunnel engineering. © 2024 Chang'an University. All rights reserved.