基于深度学习的盾构隧道渗漏水病害图像识别

doi:10.13722/j.cnki.jrme.2017.0552

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Abstract With the sharp increasing in the requirements for tunnel maintenance of urban metro，the structural defects of the metro shield tunnels，especially the water leakage，need be inspected fast and accurately. Using the computer vision to inspect the structural defects of shield tunnel is a thriving trend home and abroad during recent years. However，the traditional methods of image recognition on water leakage cannot meet the need in engineering practice. In this paper，a novel method employing the fully convolution network(FCN) based on the deep learning(DL) is proposed to improve the image recognition on water leakage in shield tunnel. The water leakage images are divided into six categories according to the lining surface of shield tunnel and some interference on image recognition. The recognition results，the error rates and the running time from FCN are compared with those from the traditional image recognition methods of Otsu algorithm(OA)，region growing algorithm(RGA) and watershed algorithm(WA). The results show that DL-based image recognition on water leakage effectively avoided the interference from the segment joints，bolt holes，cables，brackets etc.，and has excellent robustness in overcoming the defects shelter from cables.

Key words： tunnelling engineering shield tunnel water leakage deep learning image recognition

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	HUANG Hongwei1
	2
	LI Qingtong1

Cite this article:

HUANG Hongwei1,2,LI Qingtong1. Image recognition for water leakage in shield tunnel based on deep learning[J]. , 2017, 36(12): 2861-2871.

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https://rockmech.whrsm.ac.cn/EN/10.13722/j.cnki.jrme.2017.0552 OR https://rockmech.whrsm.ac.cn/EN/Y2017/V36/I12/2861

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