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基于智能手机图像获取的智能联合识别方法和地下矿山联合态度的计算
Guang Li1, Jinyao Zhu1, Changyu Jin1
1State Key Laboratory of Intelligent Deep Metal Mining and Equipment, Northeastern University, Shenyang 110819, China.
Sensors (Basel, Switzerland)
|October 29, 2025
概括
这项研究介绍了一种基于智能手机的深度学习方法,用于分析矿山中的岩石接头. 它提高了地质数据采集的效率和准确性,减少了联合态度分析中的主观性.
科学领域:
- 地质地质地质地质地质地
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 在矿山地质学中,获得共同的态度至关重要.
- 手动编目岩石连接是低效和主观的.
- 地下条件对数据采集构成重大约束.
研究的目的:
- 开发一种自动化方法,使用手机摄影和深度学习来获取岩石关节的姿态.
- 克服在地下矿山环境中手动编目目录的局限性.
- 提高地质信息获取的效率,客观性和可访问性.
主要方法:
- 采用智能手机捕获岩石关节图像并进行预处理 (增强,CLAHE增强).
- 开发了一个U-Net架构,结合ResNet和CBAM关注 (RC-Unet),用于关节的语义细分.
- 使用OpenCV和PCP三点定位算法进行了轮提取和态度计算.
主要成果:
- RC-Unet模型实现了岩石连接的精确语义细分.
- 在一个实用的工程案例中,智能关节识别方法成功取代了手动编目.
- 该方法证明了地质数据采集的提高效率和降低主观性.
结论:
- 拟议的方法为地质信息获取提供了一个快速,低成本,易于存储的解决方案.
- 这种智能方法是矿山调查的有效工具和补充方法.
- 该研究强调了深度学习在改善在具有挑战性的地下环境中的地质数据采集方面的潜力.
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