To improve the detection accuracy and efficiency of cracks during regular tunnel inspections,this study proposes an FC-ResNet algorithm for tunnel lining crack detection by using ResNet as the backbone feature extrac-tion network,incorporating U-net's"encoder-decoder"structure and optimizing network feature layers.The algo-rithm achieves pixel-level segmentation of lining cracks.To verify its effectiveness and reliability,a comparative validation was conducted using CrackSegNet and U-net.The results show that the proposed algorithm demonstrates excellent detection performance,with a pixel accuracy,mean Intersection over Union(mIoU),and Fl-score of 99.2％,87.4％,and 0.87,respectively,on the test set.These results are superior to those of CrackSegNet and U-net,and the detection time per image is 122 ms,better than CrackSegNet and comparable to the simpler U-net.Based on the FC-ResNet algorithm,an intelligent recognition system for tunnel lining cracks was developed,enabling ac-curate and fast intelligent recognition of cracks in actual tunnel engineering linings.
维普
万方数据