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: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=717611
• 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.

市政排水系统可能是最基本的基础设施之一，这些基础设施有助于使现代城市奇迹，从而改善了卫生的卫生和同样的公共保健和生活质量。实际上，公共卫生的研究显然已将卫生和个人卫生与寿命和死亡率降低相关（尤其是在婴儿中）。但是，根据全球统计数据，目前的公共基础设施（包括道路，桥梁，排水系统等）已经处于可怕的状态，并且通过更好的维护，修复，康复和替换，将其恢复到可接受的服务水平的财务需求现在数万亿美元。对于仍在水平扩展的城市而言，这种情况可能会进一步恶化，因为在这种情况下，新的基础设施将添加到现有的库存中，因此，当预测未来的维护预算进行维护，维修等预测预算时。在排水系统的情况下，结构和操作损害，结构性和操作性损害可能由多种因素引起的因素而引起的，从而导致材料的自然衰减产生材料的自然变化，而造成了缝隙和变化的缝隙，并导致了缝隙和缝隙的造成污垢，并造成了缝隙，并造成了缝隙，并造成了缝隙，并造成了缝隙，并造成了缝隙和缝隙的变化。结果，市政当局设计了一系列活动分为两类：清洁和检查，旨在保持这种基础设施一直在运行。尽管在其他检查方法方面耗时且昂贵，但CCTV是用于评估下水道状况的最重要方法。从数据分析的角度来看，构成这项研究重点的CCTV检查通常可以将其视为两步过程：（i）数据（即视频）集合，以及（ii）数据分析（即视频评估）在其中检测到缺陷的过程中。拟议的研究旨在通过建立一个自动框架来从CCTV检查视频中提取额外的价值，从中开发基于条件的劣化模型并应用以量化虚假负面因素和错误分类的后果。由于该研究的基础方法锚定在计算机视觉和图像处理领域，因此从建立与下水道管道检查相关的分析框架中获得的知识可以使用类似的检查技术来适应其他维护情况。