净水技术2025，Vol.44Issue(6)：6-16,11.DOI:10.15890/j.cnki.jsjs.2025.06.002
城市排水管道缺陷检测技术与智能识别综述:方法、对比与应用展望
Overview of Defect Inspection Technology and Intelligent Identification for Urban Drainage Pipelines:Methods,Comparisons,and Application Prospects
摘要
Abstract
With the continuous growth of the urban economy,effective monitoring and evaluation of drainage pipelines,a critical component of urban infrastructure,has become increasingly important.[Objective]This study aims to comprehensively explore drainage pipeline defect inspection method and the latest research advances in this field.[Methods]A literature review method is adopted to analyze and compare relevant studies both domestically and internationally.[Results]According to the literature review,visual inspection technology,compared to other existing defect inspection techniques,has become the mainstream method due to its unique advantages of low cost and intuitive data output.At the same time,the application of artificial intelligence models has gradually emerged,effectively improving inspection efficiency by automatically identifying and classifying visual inspection images.The research on intelligent inspection and identification mainly focuses on three areas,including image preprocessing,feature extraction,and machine learning.Image preprocessing techniques such as noise reduction and contrast enhancement improve image quality,while feature extraction method and machine learning models determine the accuracy and generalization ability of the inspection.[Conclusion]Significant progress has been made in intelligent inspection of drainage pipeline defects,but challenges remain:①Different image processing algorithms are suitable for various scenarios,and further research is needed to select the optimal algorithm for different defect types;②The complex and ever-changing pipeline environment limits the generalization ability of models,affecting the stability of defect inspection;③Deep learning models rely on large-scale,high-quality datasets,which result in high computational costs.Therefore,future research should focus on optimizing feature extraction method,enhancing the adaptability and generalization ability of algorithms,and building comprehensive defect databases to promote the development of intelligent inspection technology.关键词
排水管道/检测方法/自动识别/深度学习/管道缺陷Key words
drainage pipelines/detection method/automatic identification/deep learning/pipeline defect分类
土木建筑引用本文复制引用
沈加子,李继,韩慧丽,徐洪福,张小磊..城市排水管道缺陷检测技术与智能识别综述:方法、对比与应用展望[J].净水技术,2025,44(6):6-16,11.基金项目
深圳市科技创新委员会科技重大专项(KJZD20230923114800002) （KJZD20230923114800002）
