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

使用基于机器学习的自动化流程提高市政排水系统闭路电视检查的效率

• 批准号: RGPIN-2020-05384
• 负责人: Bouferguene, Ahmed
• 金额: $ 1.89万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=740539
• 关键词: Using; machine; learning; based; automation

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.

城市排水系统可能是最基本的基础设施之一，它为现代城市的文明奇迹做出了贡献，改善了卫生状况，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经明确地将环境卫生和个人卫生与长寿和降低死亡率（尤其是婴儿）联系起来。然而，根据世界范围的统计数据，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的财政需求现在达到数万亿美元。对于那些仍在横向扩张的城市来说，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的库存中，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，结构和操作上的损坏可由多种因素造成，包括污水气体的化学反应导致管道自然老化、土壤移动以及可能导致物质沉积的住宅和商业活动。因此，市政当局设计了一系列活动，分为两类：清洁和检查，目的是保持这一基础设施随时运转。相对于其他检测方法，CCTV是最重要的评估下水道管道状况的方法。从数据分析的角度来看，作为本研究重点的CCTV检测大致可以分为两步：一是数据（即视频）采集，二是数据分析（即视频评估），在此过程中发现缺陷并进行分类。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价值，该框架将开发一个基于状态的恶化模型，并将其应用于量化假阴性和错误分类的后果。由于本研究的基本方法是基于计算机视觉和图像处理领域，因此从构建与下水道闭路电视检查相关的分析框架中获得的知识可以适用于使用类似检查技术的其他维护情况。