题目：基于改进YOLOv8的城市排水管道缺陷检测算法研究
作者：杨 帆、刘如飞、刘扬胜、宋佰万、牛 冲、来瑞鑫
摘要：排水管道系统在城市管理中起着关键作用，为了实现排水管道缺陷的自动化检测，提出了一种基于改进 YOLOv8 的排水管道缺陷检测算法。首先针对管道图像亮度不均和网络泛化能力差的问题，采用 Zero-DCE亮度增强和图像对比度调整相结合的方法进行数据增强处理。然后通过对YOLOv8 算法添加 Coordinate Attention 注意力机制，增强算法对缺陷位置信息的感知和捕捉能力，以便于算法能够更好的识别排水管道细小缺陷。试验结果表明，相较于原始 YOLOv8 算法，改进后的算法精确度和召回率分别提升 5%和7.9%。与其他三种网络相比，精确度和召回率分别提高了5.5%、7.6%、2.2%和 7.9%、4.2%、2%。
Abstract:Drainage pipe system plays a key role in urban management, in order to realize theautomated detection of drainage pipe disease. In this paper, we propose a drainage pipe disease detection algorithm based on improved YOLOv8. First for the problem of uneven brightness of pipeine images and poor network generalization ability, data enhancement processing using a combination of Zero-DCE brightness enhancement and image contrast adjustments. Then by adding the Co.ordinate Attention (CA) attention mechanism to the YOLOv8 algorithm, enhancing the algorithms ability to perceive and capture disease location information, so that the algorithms can betteridentify minor drainage pipe defects. The experimental results show that compared to the originalYOLOv8 algorithm, the improved algorithm increases precision and recall by 5% and 7. 9% respectively, Compared to the other three networks, the precision and recall are improved by 5. 5 %7.6%, 2.2% and 7.9%, 4.2%, 2% respectively.
通讯作者：刘如飞，男，1986年出生，江苏南京人，博士，副教授，硕士研究生导师。主要研究方向为多平台激光点云数据处理、点云与图像目标特征识别、三维自动建模与 GIS应用。