The University of Kent and West Surrey Homes Limited KTP 24_25 R2

A construction company and a university are building an artificial intelligence system that can automatically inspect and enhance images of building materials, components, or completed work. This matters because construction firms currently rely on human inspectors or external specialists to check image quality and identify defects, which is slow, expensive, and inconsistent. The project aims to create a machine learning and computer vision pipeline that processes images on-site, reducing the need to send data to outside companies for analysis. By automating this step, the system could cut costs, speed up production, and catch problems earlier. If successful, the technology could change how construction sites handle quality control. Instead of waiting days for external reports, a builder might get instant feedback on whether a weld is sound, a surface is level, or a component is correctly installed. This is not about flashy consumer gadgets—it is about making the invisible infrastructure of building projects more efficient. The same approach could eventually apply to other manufacturing settings where visual inspection is critical, from car parts to electronics assembly.