在杂和形态复杂的环境中实时检测道路裂的框架.
Luxin Fan1, SaiHong Tang2, Mohd Khairol Anuar B Mohd Ariffin3
1Faculty of Engineering, Universiti Putra Malaysia UPM, Serdang, 43400, Selangor, Malaysia. gs59924@student.upm.edu.my.
Scientific reports
|February 20, 2026
概括
本研究介绍了Crack-YOLO,这是一款用于智能基础设施维护的快速而准确的道路裂检测系统. 这种轻量级模型在复杂环境中显著提高了检测性能,超过了现有的方法.
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:
- 自动道路缺陷检测对于智能交通基础设施维护至关重要.
- 现有的物体检测模型在缓慢的推断速度和低准确性方面扎,特别是在阴影,油污和遮的复杂环境中.
- 目前的模型显示,在具有挑战性的自然条件下,检测精度急剧下降.
研究的目的:
- 提出一个名为Crack-YOLO的轻量级和高精度的道路裂检测框架.
- 解决现有的道路缺陷检测系统中速度缓慢和准确度低的局限性.
- 在复杂的环境条件下提高检测性能.
主要方法:
- 开发了 Crack-YOLO,这是一个基于 YOLOv8s.s. 的新型框架.
- 将原始卷积模块替换为上下文导向 (CG) 模块.
- 实现了C2f_DynamicConv以替代静态卷积内核.
- 引入了自适应空间特征融合 (ASFF) 头来取代原来的检测头.
主要成果:
- 与YOLOv8相比,Crack-YOLO在四个数据集 (CrackVariety,CrackTree200,Crack500,CFD) 中显示出更高的检测速度和准确性.
- 在CrackVariety数据集上实现了71.4%的mAP@0.5,推断速度为416 FPS.
- 与基线YOLOv8s模型相比,精度增加了31.0%,速度增加了近两倍.
- 在Raspberry Pi 5边缘设备上成功部署了实时路面状况指数 (PCI) 计算.
结论:
- Crack-YOLO为高效准确的道路缺陷检测提供了一个实用的解决方案,即使在资源有限的边缘设备上也是如此.
- 该框架在复杂环境中保持高性能的能力标志着智能交通基础设施维护的重大进步.
- 与ASTM D6433等标准的集成使自动PCI计算成为可能,证明了现实世界的适用性.
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