A Monocular Vision-Based Safety Monitoring Framework for Offshore Infrastructures Utilizing Grounded SAM

As maritime transportation and human activities at sea continue to grow, ensuring the safety of offshore infrastructure has become an increasingly pressing research focus. However, traditional high-precision sensor systems often involve prohibitive costs, and the Automatic Identification System (AIS) faces signal loss or data manipulation problems, highlighting the need for a complementary, affordable, and reliable supplemental solution. This study introduces a monocular vision-based safety monitoring framework for offshore infrastructures. By combining advanced computer vision techniques such as Grounded SAM and horizon-based self-calibration, the proposed framework achieves accurate vessel detection, instance segmentation, and distance estimation. The model integrates open-vocabulary object detection and zero-shot segmentation, achieving high performance without additional training. To demonstrate the feasibility of the framework in practical applications, we conduct several experiments on public datasets and couple the proposed algorithms with the Leaflet.js and WebRTC libraries to develop a web-based prototype for real-time safety monitoring, providing visualized information and alerts for offshore infrastructure operators in our case study. The experimental results and case study suggest that the framework has notable advantages, including low cost, convenient deployment with minimal maintenance, high detection accuracy, and strong adaptability to diverse application conditions, which brings a supplemental solution to research on offshore infrastructure safety.