An Intelligent Detection Method for Open-Pit Slope Fracture Based on the Improved Mask R-CNN
Ruan Shunling1,2, Liu Danyang1, Gu Qinghua1,2, Jing Ying1
1Xi’an University of Architecture and Technology, Xi’an, 710055 China
2Xi'an Key Laboratory of Perception Computing and Decision Making in Intelligent Industry, Xi'an, 710055 China
Keywords: Open-pit mine, slope stability, fracture detection, segmentation, Mask R-CNN, application research
Abstract
Slope fracture detection is critical in the safety management of the open-pit mine, and the non-timely detection of slope fractures may cause landslide or other serious consequences. To prevent the unexpected accidents caused by the failure of slope integrity, this paper proposes an intelligent fracture detection algorithm based on improved Mask R-CNN, which can address the limitations of traditional image processing algorithm and the classical deep learning model directly to the open-pit mine slope crack detection. In this paper, we use the integrated features of Mask R-CNN in target detection, segmentation and location, improve the shortcomings of Mask branch, such as unclear edges and false detections, and construct a detection and segmentation framework for slope fracture images of the open-pit mine. This method introduces dilated convolution neural network, and a classify segmentation iterative up-sampling operation into the mask branch, which can solve the problem of slope fracture Mask’s rough edge. Experimental results show that compared with the traditional crack segmentation algorithm, this method has higher recognition accuracy and better segmentation effect.
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