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Pixel-Level Segmentation Method for Tunnel Lining Cracks Based on FC-ResNet Network
(1. School of Civil Engineering, Central South University, Changsha 410075; 2. China Railway Group Limited, Beijing 100039; 3. China Railway Communications Investment Croup Co., Ltd, Nanning 530219)
Abstract 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 extraction network, incorporating U-net's "encoder-decoder" structure and optimizing network feature layers. The algorithm 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 F1-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 accurate and fast intelligent recognition of cracks in actual tunnel engineering linings.
Abstract：
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 extraction network, incorporating U-net's "encoder-decoder" structure and optimizing network feature layers. The algorithm 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 F1-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 accurate and fast intelligent recognition of cracks in actual tunnel engineering linings.
HAN Fengyan1,
2 LI Huizhen3 YANG Shaojun3 GAN Fan3 XIAO Yongzhuo1
.Pixel-Level Segmentation Method for Tunnel Lining Cracks Based on FC-ResNet Network[J] MODERN TUNNELLING TECHNOLOGY, 2024,V61(5): 111-119
2024, Vol. 61 