基于FCN的岩石混凝土裂隙几何智能识别

doi:10.13722/j.cnki.jrme.2019.0010

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Abstract Efficient and accurate extraction of crack geometry is fundamental for rock or concrete engineering. Traditional algorithms of crack identification，such as threshold segmentation，edge detection and region growing method，are based on limited human experience with tedious work and low accuracy. Artificial intelligence(AI) recognition is self-circulating dependent on big data，and can be positive feedback or even self-learning. The concrete as one of many artificial materials has the similar properties with rock，especially in geometric distribution. An intelligent recognition algorithm based on full convolutional neural network is proposed to identify cracks on rock and concrete surface. The dataset of crack geometry commonly seen in three scenarios of concrete used in building structure，pavement and tunnel surface is established. Through convolution operation，pooling operation and deconvolution operation，the error is quickly reduced with training times. A new convolution kernel parameter is introduced into a new full convolutional network，and a recognition model is evaluated based on the pixel-based two-classification problem. The results shows the better recognition of the new full convolutional network than traditional algorithms. Combined with the vectorization algorithm，the real-time statistical analysis of crack geometry including length，width and area is realized. Finally，the crack identification determined by artificial intelligence and traditional algorithm is compared by arbitrarily selecting five groups of images. The cracks extracted by the new algorithm are qualitatively similar to the ground truth，and the precision and recall are higher than the traditional algorithms. The full convolutional network can continuously improve the recognition accuracy，reduce the error，and will show great vitality in the application of rock or concrete engineering.

Key words： rock mechanics crack identification traditional algorithm artificial intelligence identification fully convolutional network(FCN) precision recall

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	XUE Dongjie1
	2
	3
	4
	TANG Qichun1
	WANG Ao5
	ZHANG Liao1
	ZHOU Hongwei2
	5

Cite this article:

XUE Dongjie1,2,3, et al. FCN-based intelligent identification of crack geometry in rock or concrete[J]. , 2019, 38(S2): 3393-3403.

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https://rockmech.whrsm.ac.cn/EN/10.13722/j.cnki.jrme.2019.0010 OR https://rockmech.whrsm.ac.cn/EN/Y2019/V38/IS2/3393

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