基于计算机视觉的安全距离估计方法,以防止叉车翻转
Aqsa Sabir1, Rahat Hussain2, Muhammad Sibtain Abbas2
1Department of Computer Science and Engineering, Chung-Ang University, Korea.
概括
这项研究引入了计算机视觉系统,通过测量叉子与负载重心之间的距离来防止叉车翻转. 这种方法在检测不安全条件时达到了93%的准确性,提高了工作场所的安全性.
科学领域:
- 工程
- 计算机科学
- 工作安全
背景情况:
- 由于负载位置不当,叉车翻转在施工中会带来重大风险.
- 手动安全检查是低效的,需要持续的人员监督.
研究的目的:
- 开发基于计算机视觉的系统,用于早期检测叉车翻转的危险.
- 自动测量叉车叉和负载的重心之间的关键距离.
主要方法:
- 使用 YOLOv8 的计算机视觉来检测物体.
- 开发了一个安全距离估计模块 (SafeDEM),集成叉车长度和安全法规.
- 根据美国国家安全局的标准进行分类行为.
主要成果:
- 在识别安全/不安全场景时达到93%的分类准确度.
- 该系统以24 FPS的实时处理.
- 通过24英寸安全门进行了成功测试.
结论:
- 拟议的方法提供了一个可扩展的,非侵入性的解决方案,以提高叉车的稳定性.
- 在叉车移动之前能够提前发现危险,提高工作场所的安全性.
- 支持未来的动态部署以及静态操作.
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