Image-based segmentation and quantification of weak interlayers in rock tunnel face via deep learning

In this paper, an advanced integrated pixel-level method based on the deep convolutional neural network (DCNN) approach named DeepLabv3+ is proposed for weak interlayers detection and quantification. Furthermore, a database containing 32,040 images of limestone, dolomite, loess clay, and red clay is...

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Veröffentlicht in:	Automation in construction 2020-12, Vol.120, p.103371, Article 103371
Hauptverfasser:	Chen, Jiayao, Zhang, Dongming, Huang, Hongwei, Shadabfar, Mahdi, Zhou, Mingliang, Yang, Tongjun
Format:	Artikel
Sprache:	eng
Schlagworte:	Artificial neural networks Clay Convolutional neural network Deep learning Dolomite Image segmentation Interlayers Limestone Loess Model accuracy Pixels Rock tunnel Weak interlayer
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