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基于Rockchip RK358888的实时电缆绳检测方法
Mengpeng Qian1,2, Yong Wang3, Shaoqing Liu4
1University of Science and Technology of China, Hefei, 230026, China.
Scientific reports
|August 20, 2025
概括
本研究介绍了用于自动化煤矿的实时电缆故障检测系统. 基于Rockchip RK3588平台的Mini-YOLO模型实现了更快的速度和更高的精度,用于边缘设备.
科学领域:
- * 采矿工程 采矿工程
- * 计算机视觉 计算机视觉
- * 边缘计算 边缘计算
背景情况:
- *自动化煤矿需要自主电缆绳故障检测.
- *用于传统模型训练的基于服务器的系统是计算密集的.
- * 资源有限的边缘设备对实时缺陷检测提出了挑战.
研究的目的:
- * 为边缘设备开发实时电缆绳检测系统.
- * 加强机械化煤矿中的非破坏性钢丝绳检查.
- *为了优化Rockchip RK3588平台上的计算性能.
主要方法:
- *将MobileNetV3,Coordinate Attention (CA) 和Inner-IoU损失集成到YOLOv8框架中,从而创建了迷你YOLO.
- * C++ 线程池的实施,以加速神经网络处理单元 (NPU) 的输出.
- *使用Docker来简化环境配置和部署.
主要成果:
- *Mini-YOLO显示计算速度增加了2倍,精度比YOLOv8.2提高了1.2%.
- * 实现了每张图像大约18.5毫秒的推断时间,用于实时检测.
- * 实现了实时数据存储与定期云备份.
结论:
- * 拟议的Mini-YOLO系统有效地解决了资源有限的边缘设备上的实时缺陷检测挑战.
- * 算法组件的协同集成和优化是采矿中边缘AI的关键.
- * 该系统能够有效和可靠地自主检测自动化煤矿中电缆绳的故障.
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