摘要
随着城市经济的持续增长,排水管道作为城市基础设施的关键构成部分,其运行状态的有效监测与评估显得尤为重要。【目的】因此,研究目的在于全面了解排水管道缺陷检测方法以及在排水管道缺陷检测领域的最新研究进展。【方法】采用了文献调研的方法,对国内外相关研究进行分析比较。【结果】根据文献调研显示,视觉检测技术相较于其他现存排水管道缺陷检测技术而言,因其成本低、输出数据直观性强的独特优势已成为目前主流的检测手段。同时,人工智能模型的应用逐渐兴起,通过对视觉检测图像进行自动识别和分类,有效提高了检测效率。智能检测识别的研究主要集中于图像预处理、图像特征提取以及机器学习3个部分,其中降噪、对比度增强等图像预处理技术提升了图像质量,而特征提取方法和机器学习模型则决定了检测的准确性和泛化能力。【结论】尽管排水管道缺陷智能识别技术取得了一定进展,但仍面临挑战:①不同图像处理算法适用场景各异,如何针对不同缺陷类型选择最优算法仍需深入研究;②管道环境复杂多变,模型泛化能力不足,影响缺陷检测的稳定性;③深度学习模型依赖大规模高质量数据集,计算成本高。因此,未来研究应重点优化特征提取方法,提升算法的适应性与泛化能力,并构建完善的管道缺陷数据库,以推动智能检测技术的发展。
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: ① Different image processing algorithms are suitable for various scenarios, and further research is needed to select the optimal algorithm for different defect types;② The complex and ever-changing pipeline environment limits the generalization ability of models, affecting the stability of defect inspection;③ Deep 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.
作者
沈加子
李继
韩慧丽
徐洪福
张小磊
SHEN Jiazi;LI Ji;HAN Huili;XU Hongfu;ZHANG Xiaolei(School of Civil and Environmental Engineering,Harbin Institute of Technology,Shenzhen,Shenzhen 518055,China;Shenzhen Longgang Drainage Co.,Ltd.,Shenzhen 518026,China)
出处
《净水技术》
2025年第6期6-16,共11页
Water Purification Technology
基金
深圳市科技创新委员会科技重大专项(KJZD20230923114800002)。
关键词
排水管道
检测方法
自动识别
深度学习
管道缺陷
drainage pipelines
detection method automatic identification
deep learning
pipeline defect
作者简介
沈加子(2000-),男,硕士研究生,研究方向为排水管网技术缺陷自动识别,E-mail:17809293671@163.com;通信作者:张小磊,女,副教授,主要从事工业及生活有机废弃物资源化、城市污水厂污泥减量及资源化研究等工作,E-mail:xiaolei.zhang2016@foxmail.com。
