(1. School of Mechanics and Civil Engineering，China University of Mining and Technology(Beijing)，Beijing 100083，China；2. State Key Laboratory of Coal Resources and Safe Mining，China University of Mining and Technology(Beijing)，Beijing 100083，China；
3. State Key Laboratory of Coal Mine Disaster Dynamics and Control，Chongqing University，Chongqing 400030，China；
4. Key Laboratory of Safety and High-efficiency Coal Mining，Anhui University of Science and Technology，Huainan，
Anhui 232001，China；5. School of Energy and Mining Engineering，China University of Mining and
Technology(Beijing)，Beijing 100083，China)
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.
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2019, Vol. 38 
