Abstract: "Automation" is a key step to realize the digitization and intelligence of mines， and it is also an important guarantee for mine production safety. Due to the complex environment of the mining area， there are differences in the causes and duration of the slope deformation， which makes it extremely difficult to accurately extract the information of the slope continuous deformation. Therefore， in order to improve the accuracy of these information extraction， a method for extracting slope continuous deformation information based on depth information is proposed. Firstly， the mine slope is simulated through experiments， and the depth camera is used to record the failure process of the slope under rainfall conditions to obtain image data and three-dimensional point cloud data. Then， the camera calibration is used to establish the relationship between the image data and the 3D point cloud data， and the missing value of the point cloud is supplemented based on coordinate transformation and nearest neighbor interpolation technology.Finally， the background difference algorithm and denoising algorithm are used to identify the slope deformation area， and according to the coordinate changes of the point cloud before and after the slope deformation, the volume generated by the slope deformation is estimated. Research shows that this method can accurately identify the slope deformation area， with an average recognition accuracy of 92.39%. Machine vision technology can realize automatic identification of slope deformation area and extraction of slope deformation volume information with high accuracy， reducing manual participation.

Key words: mine slope, machine vision, deformation recognition, volume estimation