Enhancing ROV Inspection Efficiency with Computer Vision

Visual inspection of subsea oil and gas infrastructure by Remotely Operated Vehicles (ROVs) is critical for asset integrity management. However, the immense volume of video data (generated during inspections and stored in archives) poses a significant challenge in transforming raw footage into actionable, organized intelligence. This paper presents a novel software solution that automates this analysis by integrating real-time computer vision. The system detects and classifies key subsea assets, such as pipelines and specific equipment, directly from the ROV video stream. It simultaneously extracts positional metadata from the video overlay to automatically generate a geolocated, searchable database without manual intervention. The solution demonstrates dual utility. In live offshore operations, it provides real-time asset recognition and contextual data to ROV operators, drastically reducing search times and associated high operational costs. For forensic analysis, the system can automatically index and process thousands of hours of archived video. This capability translates into direct cost savings, enhances the safety and reliability of integrity management programs, and supports faster, data-driven decision-making. The solution features highly adaptable architecture, allowing for efficient model retraining to recognize new asset types, and easy integration in existing systems. Furthermore, it establishes a clear pathway for expansion, including automated anomaly detection for pipelines and integration with additional ROV-mounted sensors, marking a significant advancement toward intelligent, autonomous subsea inspection ecosystems.