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  2022, Vol. 41 Issue (10): 2082-2093    DOI: 10.13722/j.cnki.jrme.2022.0016
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Automatic identification of tunnel leakage based on deep semantic segmentation
ZHOU Zhong1,2,ZHANG Junjie1,GONG Chenjie1,2,DING Haohui1
(1. School of Civil Engineering,Central South University,Changsha,Hunan 410075,China;2. Hunan Tieyuan Civil Engineering Testing Co.,Ltd.,Changsha,Hunan 410075,China)
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Abstract  Aiming to solve the challenges of low detection accuracy,poor anti-interference ability and slow detection speed in the traditional tunnel leakage detection methods,a depth semantic segmentation algorithm for tunnel leakage is proposed on the basis of the DeepLabv3+ semantic segmentation algorithm. Firstly,the lightweight classification network EfficientNetv2 is used as the backbone network,which enhances the recognition accuracy while reduces network parameters. Secondly,Convolutional Block Attention Module(CBAM) is integrated to increase the weight of the effective channels in the image,thereby improving the ability of extraction of leakage feature information. Traditional semantic segmentation algorithms,including DeepLabv3+,PSPnet and Unet,are used for comparative experiments from three aspects:image recognition accuracy,model size,and detection speed. The results show that the mean pixel accuracy(mPA),mean intersection over union(mIoU),model size and image processing speed(FPS) of the proposed algorithm are 93.99%,89.87%,33.4 MB and 39.87 f/s,respectively. Compared with the three comparison algorithms,the detection accuracy and efficiency of the proposed algorithm have been significantly improved. Furthermore,the proposed algorithm has better edge segmentation effect and anti-interference ability,which is suitable for tunnel leakage detection tasks in complex environments,so as to better meet the needs of engineering detection.
Key wordstunnelling engineering      leakage      semantic segmentation      deep learning      neural network     
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ZHOU Zhong1
2
ZHANG Junjie1
GONG Chenjie1
2
DING Haohui1
Cite this article:   
ZHOU Zhong1,2,ZHANG Junjie1, et al. Automatic identification of tunnel leakage based on deep semantic segmentation [J]. , 2022, 41(10): 2082-2093.
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