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在不均的照明下,RDLK-YOLO增强了管道缺陷检测
Hailin Wang1, Zao Feng2,3, Mingkai Jiang4
1Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, 650500, China.
Scientific reports
|December 11, 2025
概括
本研究介绍了RDLK-YOLO,这是一个用于管道缺陷检测的先进物体检测网络. 它在具有挑战性的,不均的照明条件下显著提高了准确性和稳定性.
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:
- 有效的排水管道维护对城市基础设施至关重要.
- 传统的图像处理方法面临着不均照明的挑战,阻碍了缺陷检测.
- 在灯光不佳的情况下,管道中的退化特征需要强大的预处理技术.
研究的目的:
- 开发用于管道检查图像的照明补偿和功能增强的先进算法.
- 引入一个高效的对象检测网络,矩形可变形大型内核-YOLO (RDLK-YOLO),以改进缺陷识别.
- 提高地下管道缺陷检测的准确性和稳定性,特别是在不利的照明下.
主要方法:
- 为预处理开发了一种照明补偿和功能增强算法.
- 一个物体检测网络,矩形可变形大内核-YOLO (RDLK-YOLO),是基于YOLOv11.1设计的.
- 关键的创新包括可变形多尺度轴校准模块 (DMACM),动态大内核注意力 (DLKA),波纹转换卷积 (WT-Conv) 和混合损失函数.
主要成果:
- RDLK-YOLO实现了94.7%的精度,88%的回忆,以及92.5%的mAP@50.
- 与在不均照明下使用的现有技术相比,提出的方法显示出更高的准确性和稳定性.
- 该算法有效地解决了由复杂的照明条件引起的功能退化.
结论:
- RDLK-YOLO为现实世界管道缺陷检测提供了一个实用和有效的解决方案.
- 开发的照明补偿和功能增强技术提高了预处理质量.
- 这项研究通过先进的AI驱动检查,有助于更可靠的地下基础设施维护.
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