基于YOLOv8的轻量级交叉尺度功能融合模型用于下水道管道的缺陷检测
Ruibo Sha1, Zhifeng Zhang1, Xiao Cui1
1Software Engineering College, Zhengzhou University of Light Industry, Zhengzhou, Henan, China.
PloS one
|August 21, 2025
概括
这项研究引入了一种改进的YOLOv8模型来检测下水道管道缺陷. 改进后的模型提高了准确性和效率,使其适用于现实世界的基础设施检查.
科学领域:
- 土木工程
- 计算机视觉
- 人工智能
背景情况:
- 污水管道的完整性对城市基础设施至关重要.
- 由于多层次问题,复杂的背景,灯光变化和各种缺陷,检测下水道缺陷是具有挑战性的.
研究的目的:
- 开发一个轻量级,准确的污水管道缺陷检测模型.
- 增强现有的下水道环境物体检测模型的特征提取和融合能力.
主要方法:
- 提出基于YOLOv8的轻量级交叉尺度功能融合模型.
- 集成的C2f-FAM和HS-BiFPN模块用于改进多尺度特征提取和语义融合.
- 在复杂的环境中使用DySample进行细节捕获.
- 在下水道ML数据集上训练和评估模型,包括数据增强.
主要成果:
- 与YOLOv8n模型相比,平均精度 (mAP) 提高了3. 8%.
- 模型参数减少了35%,浮点操作减少了21%,模型大小减少了33%.
- 证明有能力区分复杂的背景和各种缺陷形态.
结论:
- 拟议的模型为排水管道缺陷检测提供了一种轻量级且准确的解决方案.
- 在具有挑战性的下水道环境中, 功能融合和细节捕获的增强是有效的.
- 该模型在城市基础设施维护方面有很大的应用潜力.
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