Using machine learning-based automation process to improve the productivity of CCTV inspections of municipal drainage systems

市政排水系统可能是最基本的基础设施之一，它有助于使现代城市成为文明的奇迹，改善卫生，同样也改善了公众健康和生活质量。事实上，公共卫生方面的研究已明确表明，环境卫生和个人卫生与长寿和降低死亡率（特别是婴儿死亡率）有关。然而，根据世界范围的统计，公共基础设施（包括道路、桥梁、排水系统等）的现状已经处于可怕的状态，通过更好的维护、修理、修复和更换使其恢复到可接受的服务水平所需的资金目前已达数万亿美元。对于仍在横向扩张的城市，这种情况可能会进一步恶化，因为在这种情况下，新的基础设施将被添加到现有库存中，因此在预测未来的维护、修理等预算时需要包括在内。在排水系统的情况下，各种因素可能会导致结构和操作损坏，包括喉管因与污水气体发生化学反应而自然老化，泥土移动，以及住宅和商业活动而可能导致物料沉积。因此，市政当局设计了一系列活动，分为两类：清洁和检查，目的是使这一基础设施随时运转。虽然与其他检查方法相比耗时且昂贵，但闭路电视是用于评估下水管道状况的最重要方法。从数据分析的角度来看，构成本研究重点的CCTV检查通常可以被视为两步过程：（i）数据（即视频）收集，以及（ii）数据分析（即视频评估），在此过程中检测和分类缺陷。拟议的研究旨在通过建立一个自动框架，从CCTV检查视频中提取额外的价值，从该框架中开发基于条件的恶化模型，并应用于量化假阴性和错误分类的后果。由于这项研究的基础方法是锚定在计算机视觉和图像处理领域，从建立与闭路电视检查下水管道相关的分析框架中获得的知识，可以适应其他维护情况下使用类似的检测技术。

Municipal drainage systems are probably one the most fundamental infrastructures which contributed to making modern cities marvels of civilisation that improved hygiene and by the same token general public health and quality of life. In fact, research in public health has clearly correlated sanitation and personal hygiene to longevity and decreased mortality (especially in infants). However, according to worldwide statistics, the current condition of public infrastructure (including roads, bridges, drainage systems, etc.) is already in a dire state and the financial requirements to bring it back to an acceptable level of service through better maintenance, repair, rehabilitation and replacement are now ranging in trillions of dollars. This situation can worsen even further for cities that are still expanding horizontally since in this case new infrastructure will be added to the existing inventory and thus will need to be included when forecasting future budgets for maintenance, repair, etc. In the case of drainage systems, structural and operational damage can be caused by a variety of factors, including natural ageing of pipes resulting from chemical reaction with sewage gases, soil movements and residential and commercial activities which can lead to material deposits. As a result, municipalities have devised a series of activities falling into two categories: cleaning and inspection, aiming at keeping this infrastructure operational at all time. Although time consuming and costly with respect to other inspection methods, CCTV is the most important method used to assess the condition of sewer pipes. From a data analysis perspective, CCTV inspection, which constitutes the focus of this research, can generically be viewed as a two-step process: (i) data (i.e. video) collection, and (ii) data analysis (i.e. video assessment) in the course of which defects are detected and classified. The proposed research aims at extracting additional value from CCTV inspection videos by building an automatic framework from which a condition-based deterioration model will be developed and applied to quantify the consequences of false negatives and misclassifications. Since the methodology underlying this research is anchored in the area of computer vision and image processing, the knowledge gained from building the analysis framework associated with CCTV inspections of sewer pipes can be adapted to other maintenance situations using a similar inspection technology.