用深度学习方法对纵向输送带裂纹进行实时分类
Uttam Kumar Dwivedi1, Ashutosh Kumar1, Yoshihide Sekimoto1
1Department of Civil Engineering, The University of Tokyo, Tokyo, Japan.
PloS one
|July 20, 2023
概括
这项研究引入了一个实时深度学习模型,用于检测和测量山地道中的输送带损伤. 该系统准确识别裂和皮带标记,提高了道施工的安全性和效率.
科学领域:
- 土木工程 土木工程是指土木工程.
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 山地道施工依赖于长型输送带进行污泥处理,这些带容易受到频繁损坏.
- 目前用于输送带损坏的视觉检查方法耗时,可能会错过关键问题.
- 需要自动损坏检测,以提高道施工的安全性和运营效率.
研究的目的:
- 开发和验证实时深度学习模型,用于检测和定位输送带的损坏.
- 准确评估检测到的损坏的大小,并识别皮带标记以准确定位.
- 为了证明该模型在边缘设备上的有效性,以便在山地道站点实际应用.
主要方法:
- 一个深度学习模型被配置为检测裂并测量输送带上的裂的大小.
- 该模型得到了增强,可以检测皮带上的数字标记,以精确地定位损坏.
- 在边缘设备上实时评估性能,测量检测精度 (mAP) 和处理速度.
主要成果:
- 优化的模型实现了0.850的平均平均精度 (mAP) 裂检测和0.99的数字标记检测.
- 该系统展示了卓越的实时性能,在边缘设备上捕捉每秒15.
- 框架成功地将损坏定位在输送带的长度上.
结论:
- 深度学习提供了一种多功能和有效的解决方案,用于在具有挑战性的山地道环境中实时评估输送带损坏.
- 拟议的模型显著提高了损坏检查的准确性和效率,减少了停机时间和维护成本.
- 这项技术验证了人工智能在提高道建设项目的安全性和运营连续性的应用.
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