An intelligent detection method for open-pit slope fracture based on the improved Mask R-CNN
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Abstract:
To prevent the unexpected accidents caused by the failure of slope integrity, we propose an intelligent fracture detection algorithm based on improved Mask R-CNN which can address the limitations of traditional image processing algorithm and the poor performance of the application of 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 to 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, solving 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.
