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Although black holes were theoretically postulated in the 1920s, they remained outside the domain of observational astronomy until the 1970s.
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煤炭和河检测网络具有紧和高性能设计.

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  • 1Collaborative Innovation Center of Steel Technology, University of Science and Technology Beijing, Beijing 100083, China.

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本研究介绍了CGDet,这是一种新的AI模型,用于在密集的采矿场景中准确地分离煤炭和. 它显著提高了检测效率,并减少了实时分类应用程序的计算负载.

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科学领域:

  • 采矿工程 采矿工程 采矿工程
  • 计算机视觉 计算机视觉
  • 人工智能的人工智能

背景情况:

  • 有效的煤炭和沟分离对于能源,环境和采矿的可持续性至关重要.
  • 机器视觉方法与密集的场景作斗争,导致标签重写问题和性能降低.
  • 在输送带上准确检测密切分布的煤炭和带仍然是一个重大挑战.

研究的目的:

  • 开发一种新的,紧的卷积神经网络 (CGDet),用于在密集的采矿环境中准确检测煤炭和.
  • 解决标签重写问题,在具有挑战性的视觉条件下提高模型性能.
  • 创建一个高效的对象检测系统,适合实时应用,计算资源有限.

主要方法:

  • 引入对象分布密度测量 (ODDM) 以优化输入和特征地图分辨率,减轻标签重写.
  • 开发了相对分辨率物体尺度测量 (RROSM) 来指导精简的特征融合结构,减少冗余.
  • 设计了CGDet,一个紧的卷积神经网络,包含ODDM和RROSM,用于增强对象检测.

主要成果:

  • CGDet以96.7%的AP50和99.2%的AR50实现了高性能.
  • 与传统模型相比,显著减少了模型参数 (46.76%),计算成本 (47.94%) 和推断时间 (31.50%).
  • 在密集的煤炭和道分离场景中表现出卓越的精度和效率.

结论:

  • CGDet有效地克服了密集场景对象检测的挑战,以进行煤炭和道分离.
  • 拟议的ODDM和RROSM方法为设计高效准确的检测网络提供了一种新的方法.
  • CGDet非常适合在资源有限的地下采矿环境中实时分类,提高运营效率和安全.