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Abstract At the request of a major offshore producer in Thailand, an analytic edge-based leak detection vision technology was developed and implemented. The solution was the result of modifications made to an onshore system that was engineered in collaboration with a midstream operator and has been proven in various aboveground facilities and other industrial monitoring applications. One of the key changes involved making the product explosion-proof (Ex). The modified AI vision system addresses 1) the unique challenges of offshore platform environments (remoteness, harsh climate, network limitations, and high safety risks), and 2) customer coverage requirements: continuous, autonomous monitoring of distributed assets with automated detection and alarming on early-stage leaks and/or leaks of a particular size. Aside from leak detection, the system can also be implemented for exhaust vents monitoring prior to well pad remote start up, fire detection and other applications. It is anticipated that the solution will deliver the benefits and advantages intended by design as well as those that were gained by onshore operators with the use of the original AI leak detection vision system. These include higher operational efficiency, improved event detection and alarm validation capabilities, enhanced automation (e.g.: remote shutdown), up to 90% workload reduction as a direct result of significantly lowered false alarms along with 50% decrease in monitoring related costs and site visits. The success of the first installation in 2019 was followed by system deployment at additional well pads, with continued expansion planned. The growing demand and the positive experience with the technology together demonstrates the viability, value and potential of artificial intelligence powered cameras for remote monitoring of offshore platform assets and processes.