On-board camera-based automatic zoning method for heading face by using computerized rock drilling cart

During construction, drilling parameters are manually adjusted by the operator, which can affect the blasting effect due to inappropriate initial parameters. To address this issue, an automatic optimal drilling method based on image partitioning of the heading face is proposed: i) Obtain images of the heading face using a suitable vehicle camera, and calculate pixel coordinates on the virtual heading face through rock drilling cart positioning and virtual heading face positioning; ii) Apply the region growth algorithm to extract the image region of the heading face, segment the image into several super-pixel units using the linear iterative clustering algorithm, followed by combining super-pixels based on the gray difference criterion. The resulting super-pixel blocks serve as the training sample set for the rock-partition method based on super-pixels and support vector machine (SVM); iii) Establish a database of drilling parameters. The results demonstrate that, compared to the region growth algorithm, the classification method based on super-pixels and SVM has higher accuracy. The algorithm has high accuracy of partition effect and good real-time performance, providing a reliable basis for optimizing the opening parameters. • An automatic optimal drilling method based on heading face image partition was proposed. • The super-pixel and support vector machine methods were used for rock partition. • The super-pixel and support vector machine methods have higher accuracy and efficiency.