Machine learning enabled remote infrastructure inspection tool using both existing and new imagery

建筑环境中的基础设施（建筑物、道路、铁路、能源结构等）需要定期检查，以确保安全。无人机、手机摄像头、闭路电视和数码相机等新技术意味着可用的资产成像数据越来越多，这使得资产检查员和管理人员越来越难以存储、审查和评估捕获的数据。但是，需要审查的数据太多，因此仍然需要进行成本高昂（有时）的实地检查，在大流行后的世界中，行动受到限制，公共和私人预算都受到限制，这将越来越困难。该项目的主要目标是开发一个完全标准化的图像和视频数据审查系统，以基于云的用户界面在已经开发和强大的机器学习技术之上交付结果。这是一项创新，因为它首次使资产管理公司和检查员能够根据需要，通过基于机器学习的候选问题和问题自动推荐，从任何连接互联网的个人电脑、家庭办公室或任何地点，审查来自无人机和车载摄像头等远程系统的大量检查数据。

Infrastructure in the built environment (buildings, roads, rail, energy structures and more) requires inspection regularly, for safety. New technologies such as drones, mobile phone cameras, CCTV and digital cameras mean that there is more and more imaging data of assets available, making it ever increasingly difficult for asset inspectors and managers to store, review and assess captured data. But there is too much data to review and so costly (and at times) physical inspections still occur, and that will be increasingly difficult in a post-pandemic world of restricted movement and both public and private budget constraints.The key objective for the project is to develop a fully standardised imagery and video data review system to deliver the results in a cloud-based user interface on top of an already developed and powerful ML technology. This is innovative as for the first time it will enable asset managers and inspectors to review very high volumes of inspection data from remote systems such as drones and on-vehicle cameras on an exception basis via ML based automatic recommendation of candidate issues and problems, and from any internet-connected personal computer, in home office or at any location as required.