Full automation of sewer CCTV surveys

下水道闭路电视调查全自动化

• 批准号: MR/V024655/1
• 负责人: Joshua Myrans
• 金额: $ 36.43万
• 依托单位: Pennon Group (United Kingdom)
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FV024655%2F1
• 关键词: Full; automation; sewer; CCTV; surveys

Water companies across the UK (and world) regularly inspect their sewers to prioritise maintenance and ensure the effective operation of their network. Failure to do so can result in incidents, including the discharge of untreated sewage to the environment, pipe collapse or even the formation of sewer blocking fatbergs. The importance of minimising these events is reinforced by the UKWIR objective to achieve zero uncontrolled sewer discharges by 2050. In most cases these occurrences are prevented using CCTV surveying and resolved with an early intervention. However, surveys are time consuming and expensive. Moreover, these reports are often inconsistent and inaccurate, largely due to human error and the subjective nature of fault codes. This project aims to augment the existing annotation and reporting process, with the overall ambition of fully automating the full CCTV surveying process. This proposed combination of AI and robotics will revolutionise sewer surveying and maintenance, improving the speed accuracy and efficiency of the entire practice. In turn this should result in the completion of more surveys and a much higher chance of pre-empting sewer failure.Currently SWW and the UoE are completing a KTP project, to internally implement the prototype fault detection method, investigated during the preceding PhD. The two-year partnership (due to complete in November 2020), has developed and trained the detection system on SWW's archive of CCTV footage and implementing this as a decision support tool. This is capable of highlighting faults and estimating their general type from recorded CCTV footage; extremely useful for the quick analysis of previously unused video that lacks annotation. Alongside technical developments, the project has built a network of collaborators (including iTouch and the WRc), whilst being widely publicised at both academic and industry events. Although the KTP has achieved its goal of bringing a functional tool to SWW, it is clear that the technology has potential for so much more, driving up efficiency and accuracy over current practices. The three key goals of the project are:(1) Develop the annotation capabilities of the technology to achieve the full standards outlined in the MSCC.(2) Implement the developed software so as to assist and perform live reporting.(3) Record and annotate previously unreported pipe features.The proposed project offers the opportunity to not only develop this research into a fully flourished technology for both UK and international use, but provides the resources and foundations for future image processing and machine learning research within SWW and the water industry as a whole. This research would continue to contribute solutions to national and global initiatives, aligning with the UN sustainable development goal ('protecting important sites for terrestrial and freshwater biodiversity'), UKWIR's Big Questions ('How do we achieve zero uncontrolled discharges from sewers by 2050?') and the UK industrial Strategy ('Increase sector productivity utilising AI'). Whether this takes the form of future visual inspection techniques or automation and support of other operational functions, the work would continue to drive efficiencies and improve performance using cutting edge computer science techniques.

英国（和世界）的水务公司定期检查他们的下水道，以优先维护和确保他们的网络有效运行。如果不这样做，可能会导致事故，包括未经处理的污水排放到环境中，管道坍塌，甚至形成下水道堵塞的父亲。UKWIR的目标是到2050年实现无控制的污水零排放，这加强了将这些事件最小化的重要性。在大多数情况下，使用闭路电视测量可以预防这些情况，并通过早期干预解决。然而，调查既耗时又昂贵。此外，这些报告经常是不一致和不准确的，主要是由于人为错误和故障代码的主观性质。该项目旨在增强现有的注释和报告流程，实现整个CCTV测量流程的完全自动化。人工智能和机器人技术的结合将彻底改变下水道测量和维护，提高整个实践的速度、准确性和效率。反过来，这将导致完成更多的调查，并有更高的机会预防下水道故障。目前，SWW和UoE正在完成一个KTP项目，在内部实现原型故障检测方法，在之前的博士研究期间。这项为期两年的合作伙伴关系（将于2020年11月完成）开发和培训了SWW的闭路电视录像档案的检测系统，并将其作为决策支持工具实施。这是能够突出故障和估计他们的一般类型从记录的闭路电视镜头；非常有用的快速分析以前未使用的视频，缺乏注释。随着技术的发展，该项目已经建立了一个合作者网络（包括iTouch和WRc），同时在学术和行业活动中广泛宣传。虽然KTP已经实现了为SWW带来一个功能工具的目标，但很明显，该技术还有更多的潜力，可以提高效率和准确性。该项目的三个关键目标是：(1)开发该技术的注释能力，以实现MSCC中概述的全部标准。(2)实施开发的软件，协助现场报道。(3)记录和注释以前未报告的管道特征。拟议的项目不仅提供了将这项研究发展成为英国和国际应用的全面繁荣技术的机会，而且为SWW和整个水务行业的未来图像处理和机器学习研究提供了资源和基础。这项研究将继续为国家和全球倡议提供解决方案，与联合国可持续发展目标（“保护陆地和淡水生物多样性的重要地点”）、UKWIR的大问题（“我们如何在2050年前实现下水道零无控制排放？”）保持一致。)和英国工业战略（“利用人工智能提高部门生产率”）。无论是采用未来的视觉检查技术，还是自动化和其他操作功能的支持，这项工作都将继续使用尖端的计算机科学技术来提高效率和提高性能。