使用带有旋转边界框的检测模型检测路面异常的比较研究.
Shunli Ji1, Fusheng Niu2, Yazhou Qin3
1Department of Ship and Ocean Engineering, Jiangsu Shipping College, Nantong, China.
PloS one
|August 12, 2025
概括
这项研究引入了自动路面异常检测的旋转矩形标签,显著提高了准确性. 旋转的YOLOv4-ResNet50模型实现了0.742 mAP,超过了检测裂和坑洞的其他模型.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 土木工程 土木工程是指土木工程.
背景情况:
- 自动路面异常检测对于基础设施维护至关重要.
- 像YOLOv4-Tiny这样的现有模型在检测路面缺陷时的准确性有局限性.
- 路面上的异常,如裂和坑洞,需要精确的识别才能进行有效的修复.
研究的目的:
- 为了提高自动化路面异常检测模型的准确性.
- 引入和评估一种新的旋转矩形标签策略.
- 为了比较基于YOLOv4的不同模型的性能,有和没有旋转边界框.
主要方法:
- 开发并比较了YOLOv4-Tiny和YOLOv4-ResNet50模型.
- 引入了旋转矩形界限框策略,用于标记路面异常.
- 在来自中国南通的1107个裂和691个坑洞的数据集上评估模型.
主要成果:
- 基线YOLOv4-Tiny模型实现了低于0.4.4的平均平均精度 (mAP).
- 旋转YOLOv4-ResNet50的模型实现了0.742.50的优越的mAP.
- 旋转的边界框与轴对齐的框相比,可以更好地检测倾斜的裂和坑洞.
结论:
- 旋转矩形标签显著提高了路面异常检测的准确性.
- 旋转的YOLOv4-ResNet50模型在检测路面缺陷方面展示了最先进的性能.
- 这种方法为更准确,更强大的基础设施监控系统提供了基础.
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