基于深度语义分割的隧道渗漏水智能识别

doi:10.13722/j.cnki.jrme.2022.0016

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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 words： tunnelling engineering leakage semantic segmentation deep learning neural network

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	Articles by authors
	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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https://rockmech.whrsm.ac.cn/EN/10.13722/j.cnki.jrme.2022.0016 OR https://rockmech.whrsm.ac.cn/EN/Y2022/V41/I10/2082

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