基于改进的YOLOv5s,检测铁路电缆绝缘体缺陷
Jing Tang1,2, Minghui Yu2, Minghu Wu1,2
1Hubei Key Laboratory for High-efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology, Wuhan, China.
PeerJ. Computer science
|August 7, 2023
概括
一种名为RCID-YOLOv5s的新方法可以准确检测铁路电缆绝缘体缺陷. 这种方法提高了对小缺陷的检测能力,提高了铁路安全和维护效率.
科学领域:
- 铁路工程 铁路工程是指铁路工程.
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 铁路电缆绝缘器对于安全运行至关重要.
- 这些绝缘体的缺陷可能导致服务中断和安全隐患.
- 自动缺陷检测对于高效的维护至关重要.
研究的目的:
- 提出一种改进的方法来检测铁路连锁电缆绝缘体的缺陷.
- 提高自动缺陷检测系统的准确性和效率.
- 为了特别应对检测小缺陷的挑战.
主要方法:
- 开发RCID-YOLOv5s模型,集成一个小物体检测层.
- 整合三重注意 (TA) 模块,专注于缺陷区域.
- 修剪操作的应用以减少计算复杂性.
- 与原始YOLOv5s模型进行比较分析.
主要成果:
- 拟议的RCID-YOLOv5s模型实现了最高的平均精度 (AP) 98.0%.
- 与原始YOLOv5s相比,在检测缺陷方面表现出卓越的性能.
- 成功提高了网络检测小缺陷部件的能力.
结论:
- RCID-YOLOv5s是一种高精度和高效的铁路连锁绝缘体缺陷检测方法.
- 集成特定的模块和优化技术显著提高了检测能力.
- 该方法为铁路基础设施的自动检查和维护提供了可靠的解决方案.
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