基于视觉的实时螺栓松检测通过识别反松线路来检测松
Wenyang Lei1,2, Fang Yuan1,2, Jiang Guo1,2
1School of Power and Mechanical Engineering, Wuhan University, Wuhan 430072, China.
Sensors (Basel, Switzerland)
|October 26, 2024
概括
本研究介绍了一种基于视觉的方法,用于使用人工智能实时检测螺栓松动. 它通过分析抗松动标记器准确地识别松动螺栓,确保设备的安全.
科学领域:
- 机械工程 机械工程
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 螺栓松动是操作设备的关键安全问题.
- 现有的检测方法往往缺乏实时功能或需要物理接触.
研究的目的:
- 开发一种非接触式,基于实时视觉的系统,用于检测螺栓松动.
- 准确测量螺栓松动角度使用防松动线标记.
主要方法:
- 使用YOLOv10-S进行实时螺栓检测和Fast-SCNN进行图像分割.
- 采用圆/线拟合和空间投影用于角度计算.
- 处理了高分辨率图像 (2048 × 1024 像素).
主要成果:
- 实现了1.145°的平均角度检测误差.
- 显示了32 FPS的高检测速度.
- 展示了适应各种螺栓类型和配置的适应性.
结论:
- 提出的基于视觉的方法提供了一个可靠的,非接触式解决方案,用于实时检测螺栓松动.
- 与传统方法相比,这种方法显著减少了检测错误.
- 该系统显示了提高设备安全和维护的巨大潜力.
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