An AI-Assisted Thermal Monitoring System for Maritime Safety: A Case Study at the Port of Nazaré

Harbour bar entrances present severe maritime safety risks, particularly under low-light and adverse weather conditions where conventional visible-spectrum RGB (Red, Green, and Blue) cameras lose effectiveness. This Communication presents an advanced, 24/7 thermal monitoring system deployed at the Port of Nazaré, Portugal, developed within the framework of the European BRIGHTER project. Combining Long-Wave Infrared (LWIR) sensors with hybrid Artificial Intelligence (AI)—integrating computer vision, heuristic rules, and YOLOv7-based Convolutional Neural Networks (CNNs)—the system achieves continuous real-time detection and classification of fishing vessels, recreational boats, people, and birds. The hardware architecture links two remote camera sites via a hybrid network (fibre optics and long-range Wi-Fi) to a centralised Communication Centre powered by a high-performance Central Processing Unit (CPU) and Graphics Processing Unit (GPU). Over a two-year operational period, the system collected a domain-specific dataset of over 48,000 annotated thermal images. Results demonstrate high target precision, robust persistent tracking using the Hungarian algorithm, and automated JavaScript Object Notation (JSON) metadata generation for triggering intelligent geofenced alarms.