基于爆堆煤岩图像边缘优化检测算法的大块率评价方法
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黑龙江科技大学引进高层次人才科研启动基金资助项目(编号:HKDQDJ202403);黑龙江省“揭榜挂帅”科技攻关项目(2021ZXJ02A03,2021ZXJ02A04)


A Method for Evaluating Large Block Rates Based on Edge-Optimized Detection Algorithm for Blasted Coal-Rock Images
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    摘要:

    爆堆大块率是评价爆破效果的主要指标,爆堆大块率的检测技术和识别方法可以快速量化并反馈矿山爆破质量。针对矿石块度识别图像出现伪边缘或缺失,致使矿山测量大块率精度偏低的现象,提出一种基于Canny算子的大块率测定方法。以元宝山露天矿爆堆图像为数据源,首先利用统计滤波算法解决图像边缘缺失的问题,然后采用自适应阈值算法和3×3梯度边缘检测算法分割图像,再利用形态学和膨胀腐蚀算法去除伪边缘,进一步融合两种结果,最后使用自然迭代、形态闭合和细化实现矿石图像的不连续边缘,进而优化分割。研究结果表明,该方法能够实现图像的精准分割,提高爆破大块率检测结果的准确性。

    Abstract:

    The establishment of fast and efficient blasting fragmentation detection technology and identification method can quickly quantify and feedback the blasting effect. Aiming at the false edges or missing edges in the edge detection operator of blasted ore images, this paper proposes an Improved Canny operator edge detection method.Using images of blast piles from Yuanbaoshan Open Pit Mine as the data source, firstly, statistical filtering is used to improve the image edge blur problem that may be caused by Gaussian filtering when denoising; secondly, the gradient amplitude and gradient direction of the burst image are calculated based on the 3×3 gradient template of the operator; finally, based on the natural iteration method, the image threshold is automatically obtained, and the discontinuous edge is repaired by the morphological closing operation and thinning operator to obtain the final detection edge.The results demonstrate that this method achieves precise image segmentation, enhancing the accuracy of large block rate detection in blasting operations.

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张鹏飞,余志炎,白润才,等. 基于爆堆煤岩图像边缘优化检测算法的大块率评价方法[J]. 科学技术与工程, 2026, 26(27): 11674-11684.
Zhang Pengfei, Yu Zhiyan, Bai Runcai, et al. A Method for Evaluating Large Block Rates Based on Edge-Optimized Detection Algorithm for Blasted Coal-Rock Images[J]. Science Technology and Engineering,2026,26(27):11674-11684.

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  • 收稿日期:2025-10-17
  • 最后修改日期:2026-07-02
  • 录用日期:2026-02-06
  • 在线发布日期: 2026-09-30
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