Video-Powered AI Solutions for Active Monitoring and Safety Improvement in Offshore Environments: The BRAM Offshore and Altave Case

Abstract This paper explores the utilization of video-based artificial intelligence (AI) tools for enhancing safety measures in offshore environments, specifically within the Bram Offshore fleet in Brazil. The study focuses on the application of AI-powered systems in monitoring and analyzing critical activities, identifying potential risks, and preventing hazardous incidents. It highlights the development and implementation of advanced AI algorithms integrated with video monitoring technology, showcasing their effectiveness in real-time risk detection and mitigation. By monitoring Personal Protective Equipment (PPE) compliance and managing Red Zone areas, these AI solutions significantly improve safety protocols and incident prevention. High-resolution cameras capture detailed images of operations, and the resulting dataset is used to train deep learning algorithms through techniques such as segmentation, classification, object detection, pose estimation, and tracking. Real-time insights are presented on a web platform as alerts, reports, and dashboards, with audible alarms for critical scenarios to ensure immediate interventions. The system's effectiveness is demonstrated through data from the Reedbuck vessel during the last year of operation. Key findings include enhanced hazard detection and improved safety, establishing video-based AI tools as a transformative approach to safety management in the offshore industry.