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采矿带外体检测方法基于YOLOv4_GECA模型.

Dong Xiao1,2, Panpan Liu3,4, Jichun Wang5,6

  • 1Information Science and Engineering School, Northeastern University, Shenyang, 110004, China. xiaodong@ise.neu.edu.cn.

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概括

在采矿带中检测异物对于安全和效率至关重要. 拟议的YOLOv4_GECA方法达到90.1%的准确性和30ms的检测时间,满足实时采矿安全要求.

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科学领域:

  • 工程 工程师 工程师 工程师
  • 计算机科学 计算机科学
  • 人工智能的人工智能

背景情况:

  • 采矿带输送器容易受到外来物体的入侵,对设备和人员构成风险.
  • 早期检测异物对于防止生产延迟和采矿操作中的安全隐患至关重要.

研究的目的:

  • 开发一种有效的方法来检测采矿带输送系统中的异物.
  • 为了提高外来物体检测的准确性和实时性能.

主要方法:

  • 实施YOLOv4_GECA模型,结合GECA注意模块来改进特征提取.
  • 使用重启cosine化学习速率衰减来优化检测性能.
  • 从金矿现场创建一个专门的异物检测数据集.

主要成果:

  • 通过YOLOv4_GECA方法实现了90.1%的平均检测准确度.
  • 记录了90.7%的召回率.
  • 该模型显示平均检测时间为30毫秒,确保实时功能.

结论:

  • YOLOv4_GECA方法有效地检测矿山带运输中的异物.
  • 拟议的方法符合工业采矿环境中对准确性和实时性能的严格要求.