Automatic recognition and detection method of rock block based on machine vision
In the process of tunnel construction,the acquisition of rock mass parameters is the premise of parameter adjustment of tunnel boring machine and intelligent decision-making,so necessary to sample and detect the rock blocks in tunneling.However,the recognition and detection of rock blocks are mainly done manually at present.In order to solve the problem of automatic recognition and detection of rock blocks,this paper presents an automatic recognition and detection of rock block parameters based on machine vision,which can quickly and accurately obtain the centroid coordinates and the minimum diameter of rock block through centroid by fusing rock block region detection and semantic segmentation algorithm.Firstly,the YOLOv3 network is used to identify the rock block and realize the region detection of the rock block.Then,FCN-DenseNet network is used for semantic segmentation and image processing of rock blocks in each area.By improving the total convolutional neural network,the number of parameters of semantic segmentation model is reduced,the efficiency of semantic segmentation is improved,and the accuracy and speed of rock block contour acquisition are also improved.Finally,the centroid coordinates and the minimum diameter passing through the centroid are calculated according to the contour points of the rock block,which provides support for grasping by the mechanical arm and calculating the load strength of the rock block point.The experimental platform is built,the hand-eye calibration of the robot arm is completed,the rock image is aligned with the point cloud of rock block under the coordinates of the depth camera,and the position of the rock centroid coordinates under the coordinates of the robot arm is obtained.The experimental results show that the proposed algorithm can quickly and accurately obtain the shape and position parameters of rock blocks,and the successful rate of recognition and detection of 102 rock blocks in 10 experiments is 91.18%,and the successful rate of suction of all rock blocks is 92.47%.It can be applied to automatic detection of rock mass parameters to improve the efficiency and intelligent level of rock mass parameter detection
rock block recognitionarea detectionsemantic segmentationrock block locationpoint cloud alignment
NETL
NSTL
万方数据
