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，它使现代城市成为文明奇迹，改善了卫生，同样也改善了一般公共健康和生活质量。事实上，公共卫生方面的研究已经明确地将卫生和个人卫生与长寿和降低死亡率(特别是婴儿)联系在一起。然而，据全球统计，目前公共基础设施(包括道路、桥梁、排水系统等)的状况。它已经处于可怕的状态，通过更好的维护、维修、恢复和更换将其恢复到可接受的服务水平所需的资金现在以万亿美元计。对于仍在水平扩张的城市来说，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、维修等预算时需要包括在内。就排水系统而言，结构和运营的损害可能由各种因素造成，包括与污水气体的化学反应导致的管道自然老化、土壤流动以及可能导致材料沉积的住宅和商业活动。因此，市政当局制定了一系列活动，分为两类：清洁和检查，旨在使这一基础设施始终保持运转。虽然与其他检测方法相比，CCTV既耗时又昂贵，但它是用于评估下水道管道状况的最重要的方法。从数据分析的角度来看，闭路电视检查是本研究的重点，一般可视为一个两步过程：(I)数据(即视频)收集和(Ii)数据分析(即视频评估)在此过程中发现缺陷并对其进行分类。这项拟议的研究旨在通过建立一个自动框架来从闭路电视检查视频中提取附加价值，该框架将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。由于这项研究的方法植根于计算机视觉和图像处理领域，因此，通过建立与污水管道闭路电视检测相关的分析框架所获得的知识可以适用于使用类似检测技术的其他维护情况。

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.