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

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.