MineVisual:煤矿中的一个无电池的视觉感知系统
Ming Li1, Zhongxu Bao1, Shuting Li1
1School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China.
Sensors (Basel, Switzerland)
|September 13, 2025
概括
MineVisual为地下煤矿提供无电池的视觉传感,使用轻量级的深度神经网络和能量意识的修剪,在恶劣环境中进行高效,准确的安全监控.
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
- 采矿工程 采矿工程 采矿工程
背景情况:
- 地下煤矿需要强大的安全监控系统.
- 传统方法受到能源供应和维护需求的限制.
- 高的计算需求和能源限制挑战了现有的解决方案.
研究的目的:
- 提出MineVisual,一个用于地下煤矿的无电池视觉传感方案.
- 在资源有限的环境中解决能源限制和计算需求.
- 在复杂的地下环境中实现智能安全监控.
主要方法:
- 开发了一个优化的轻量级深度神经网络,具有深度可分离的卷积.
- 引入了能源意识的动态修剪网络 (EADP-Net),以保持持续的准确性.
- 集成的超级电容器缓冲和电压调节,以确保稳定的运行.
主要成果:
- 在特定于矿井的任务中,MineVisual 实现了高精度 (91.5% Top-1).
- 显著提高了能源效率,将推理能量减少到6.89mJ.
- 在不同的风速和波动的功率条件下证明了强度.
结论:
- MineVisual为智能安全监控提供了一个有效的技术途径.
- 在煤矿等极端环境中证明了无电池深度学习推断的可行性.
- 在具有挑战性的地下环境中实现持续的性能和能源效率.
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