改进了基于YOLOv8的目标精度检测算法,用于检测火车车轮步道缺陷
Yu Wen1, Xiaorong Gao1, Lin Luo1
1School of Physical Science and Technology, Southwest Jiaotong University, Chengdu 610031, China.
Sensors (Basel, Switzerland)
|June 19, 2024
概括
这项研究增强了YOLOv8模型,用于准确检测火车轮缺陷,提高了安全性和维护效率. 优化的模型有效地识别缺陷,即使是小的缺陷,并减少从水污点的错误检测.
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 计算机视觉
- 人工智能的人工智能是人工智能.
背景情况:
- 列车轮的完整性对于运行安全至关重要.
- 目前基于图像的缺陷检测面临着水污点和小缺陷的挑战.
- 及时识别缺陷使得有条件的修复策略成为可能.
研究的目的:
- 为了提高火车轮胎面积缺陷检测的准确性和效率.
- 解决现有方法的局限性,特别是错误检测水污点和小缺陷.
- 为了增强YOLOv8模型,在识别轮胎滑面异常方面提供卓越的性能.
主要方法:
- 优化了检测层结构,以减轻水污染干扰.
- 引入了一个改进的空间金字塔聚合跨阶段部分 (SPPCSPC) 模块,用于增强小目标检测.
- 实现SIoU损失函数以加快网络融合并提高准确性.
- 在定制的轮胎胎缺陷数据集上验证了增强的YOLOv8模型.
主要成果:
- 增强的YOLOv8模型显著超过了原始网络的性能.
- 实现了高检测性能,平均精度为96.95%,准确度为96.30%,回忆率为95.31%.
- 展示了强大的检测能力,克服了水污点和小缺陷带来的挑战.
结论:
- 拟议的YOLOv8增强为火车轮流面缺陷检测提供了更可靠,更有效的解决方案.
- 优化的模型通过准确和及时的维护,有助于提高铁路安全.
- 这种方法为铁路行业的先进自动化检查系统提供了坚实的基础.
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