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Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
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岩石爆炸评估 - 基于深度学习的图像识别方法.

Haibao Yi1,2, Aixiang Wu3, Xiliang Zhang4,5

  • 1School of Resources and Safety Engineering, University of Science and Technology Beijing, Beijing, 100083, China. hang_tianfeiji@126.com.

Scientific reports
|July 4, 2025
PubMed
概括
此摘要是机器生成的。

图像识别能够准确地评估岩石爆破的质量,比手工方法提高效率和准确性. 这项技术有助于优化爆炸参数,以改善岩石碎片和装载.

关键词:
爆破碎片化的爆破碎片化扫雷质量评价 扫雷质量评价爆破半孔的半孔速度.图像识别 图像识别 图像识别机器深度学习 机器深度学习

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科学领域:

  • 地质技术工程 地质技术工程
  • 采矿工程 采矿工程 采矿工程
  • 计算机视觉 计算机视觉

背景情况:

  • 对地质技术和采矿操作来说,评估岩石爆破质量至关重要.
  • 传统的手动分析爆破碎片和半孔率是耗时且容易出现错误的.

研究的目的:

  • 开发和验证一种图像分析模型,用于评估岩石爆破碎片和半孔率.
  • 提高矿山爆破质量评估的效率和准确性.

主要方法:

  • 使用机器学习开发了爆炸效应图像分析和计算模型.
  • 实现了一种图像识别算法,用于分析分离前喷射碎片和半孔率.
  • 安装了皮尔森曲线函数,并提出了一个细分的R-R区块大小分布校正模型.

主要成果:

  • 图像识别实现了67.15%的半孔率,与手动统计数据 (68.16%) 相比,平均误差为1.49%.
  • 确定了矿石岩块大小的"S"形累积分布模式.
  • 拟议的模型准确地描述了爆破区块大小分布.

结论:

  • 图像识别技术提供了一种可靠和高效的方法来评估分裂前的喷射质量.
  • 开发的模型为调整喷气参数和改善现场喷气结果提供了有价值的反.
  • 这种方法大大克服了传统手工分析的局限性,显示出很大的应用潜力.