Robert Gordon University and Fennex Limited KTP 24_25 R2

A computer vision system will learn to spot dangers on offshore oil and gas platforms before they cause accidents. Current hazard detection on offshore installations relies heavily on human observation and manual inspection, which is slow, expensive, and prone to error. Workers must monitor complex environments where equipment failures, gas leaks, or structural issues can escalate quickly. This project aims to replace that reactive approach with an AI-powered system that continuously analyses video feeds to identify and predict hazards in real time. If successful, the technology could reduce accidents and downtime across the energy industry, from oil and gas to emerging offshore wind farms. The system would also be customisable for different operational environments, allowing the company to sell it internationally. For the public, the impact is indirect but significant: safer offshore operations mean fewer environmental disasters, more reliable energy supplies, and lower costs passed on to consumers. The project is a commercial partnership between a university and a company, focused on developing a market-ready product rather than answering fundamental scientific questions.