![]() ![]() ![]() The 3D point cloud data is mainly obtained using 3D laser scans or a large number of high-resolution digital photos taken by Unmanned Aerial Vehicle (UAV) 13, 14. The existing measurement methods can be summarized into two categories: three-dimensional (3D) point cloud data segmentation measurement and two-dimensional (2D) image segmentation measurement. Blasted rock piles are characterized by large scale, serious adhesion and irregularly shaped rock clumps, large differences in particle size, and small differences in grayness, which make it difficult to accurately measure the particle size of blasted rocks 11, 12. Therefore, it is of theoretical significance and practical value to establish a fast and accurate detection method for particle size of rock fragmentation to guide blasting construction and improve blasting efficiency. ![]() As an important technical indicator of blasting effectiveness, blasted block size distribution directly affects the cost and efficiency of subsequent shoveling, crushing and grinding processes, and also provides a necessary basis for blasting parameter optimization 6, 7, 8, 9, 10. Similar content being viewed by othersīlasting is widely used in mining and civil engineering due to its economy and efficiency 1, 2, 3, 4, 5. The method provides a new idea for particle segmentation in other fields, which has good application and promotion value. The area cumulative distribution curve of the segmentation result is highly consistent with the manual segmentation, and the segmentation accuracy was above 95.65% for both limestone and granite for rock blocks with area over 100 cm 2, indicating that the algorithm can accurately perform seed point marking and watershed segmentation for blasted rock image, and effectively reduce the possibility of incorrect segmentation. The algorithm first obtains the binary image after image pre-processing and performs distance transformation then by selecting the appropriate gray threshold, the adherent part of the distance transformation image, i.e., the adherent rock blocks in the blasted rock image, is segmented and the seed points are marked based on the solidity of the contour calculated by contour detection finally, the watershed algorithm is used to segment. This study introduces the Phansalkar binarization method, proposes the watershed seed point marking method based on the solidity of rock block contour, and forms an adaptive watershed segmentation algorithm for blasted rock piles images based on rock block shape, which is to better solve the problem of incorrect segmentation caused by adhesion, stacking and blurred edges in blasted rock images. It is of great theoretical significance and practical value to establish a fast and accurate detection method for particle size of rock fragmentation. ![]()
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