面向漂流胶囊机器人图像的管道缺陷检测  

Pipeline defect detection method for floating capsule robot image

作　　者：朱松 陈安泰 华远盛 姜文宇 元鹏鹏 朱家松[1,3] ZHU Song;CHEN Antai;HUA Yuansheng;JIANG Wenyu;YUAN Pengpeng;ZHU Jiasong(School of Civil and Transportation Engineering,Shenzhen University,Shenzhen 518060,China;Shenzhen Zhiyuan Space Innovation Technology Co.,Ltd.,Shenzhen 518057,China;National Key Laboratory of Green and Longevity Road Engineering in Extreme Environments,Shenzhen 518060,China)

机构地区：[1]深圳大学土木与交通工程学院,广东深圳518060 [2]深圳市智源空间创新科技有限公司,广东深圳518057 [3]极端环境绿色长寿道路工程全国重点实验室,广东深圳518060

出　　处：《测绘通报》2025年第4期34-38,共5页Bulletin of Surveying and Mapping

基　　金：深圳市科技创新委项目(20231120191328001);广东省区域联合基金青年项目(2023A1515110722);国家自然科学基金(41871329);国家重点研发计划子课题(2018YFB2101005)。

摘　　要：城市地下给排水系统的安全运维对社会经济的可持续发展至关重要。管道漂流胶囊机器人作为一种新型的自动化检测工具,能够有效解决传统方法存在的成本高、效率低等问题。然而,管道内流水扰动、设备自热等因素会导致胶囊机器人采集的图像存在水雾干扰,严重影响病害识别精度。因此,本文设计了一种基于双支特征融合去水雾的轻量级缺陷检测网络,以提高低质量胶囊机器人的图像质量和缺陷识别的准确性。其中,双支去水雾模块自适应提取并融合图像空间结构和光谱特征,增强图像水雾消除性能;轻量级检测模块以YOLOv5为主干,对去水雾后的图像中管道缺陷类型及损坏区域进行识别和定位。在SFCRI数据集上的试验结果表明,去水雾模块使图像的SSIM和PSNR分别提升了0.271和24.04,缺陷识别全类平均精度提高了近12%,识别速度达120.3帧/s。本文提出的基于双支去水雾模块的轻量级缺陷检测网络可以有效实现城市地下给排水管道高效率和低成本缺陷检测。The safe operation and maintenance of urban underground water supply and drainage systems are crucial for the sustainable development of society and the economy.The drifting capsule robot,as a new type of automated inspection tool,can effectively address the high costs and low efficiency of traditional methods.However,factors such as water flow disturbances inside the pipeline and the self-heating of the equipment can cause water mist interference in the images collected by the capsule robot,severely affecting the accuracy of defect identification.Therefore,this paper designs a lightweight defect detection network based on dual-branch feature fusion dehazing to improve the image quality and defect identification accuracy of low-quality capsule robot images.The dual-branch dehazing module adaptively extracts and fuses the spatial structure and spectral features of the image,enhancing the image s dehazing performance.The lightweight detection module,with YOLOv5 as the backbone,identifies and locates pipeline defect types and damaged areas in the dehazed images.Experimental results on the SFCRI dataset show that the dehazing module improves the SSIM and PSNR of the images by 0.271 and 24.04,respectively,and increases the average precision of defect identification by nearly 12%,achieving a recognition speed of 120.3 frame/s.The proposed lightweight defect detection network based on the dual-branch dehazing module can effectively achieve high-efficiency and low-cost defect detection for urban underground water supply and drainage pipelines.

关 键 词：管道缺陷检测 漂流胶囊机器人 双支特征融合水雾 深度学习 

分 类 号：P234[天文地球—摄影测量与遥感]