Reducing Collision Risks in Harbours with Mixed AIS and Non-AIS Traffic Using Augmented Reality and ANN

Ports with Mediterranean-like traffic profiles combine dense passenger, cargo, touristic, and local operations in confined waters where many small craft sail without AIS, increasing collision risk. Nature of such traffic in often unpredictable, due to often and sudden course corrections or changes. In such situations, it is possible that larger ships cannot manoeuvre to avoid collisions with small vessels. Hence, it is important to the port authority to develop a fast and adoptable mean to reduce collision risks. We present an end-to-end shore-based framework that detects and tracks vessels from fixed cameras (YOLOv9 + DeepSORT), estimates speed from monocular lateral video with an artificial neural network (ANN), and visualises collision risk in augmented reality (AR) for VTS/port operators. Validation in the Port of Split using laser rangefinder/GPS ground truth yields MAE 1.98 km/h and RMSE 2.18 km/h (0.605 m/s), with relative errors 2.83–21.97% across vessel classes. We discuss limitations (sample size, weather), failure modes, and deployment pathways. The application uses stationary port camera as an input. The core calculations are performed at user’s computer in the building. Mobile application uses wireless communication to show risk assessment at augmented reality smart phone. For training of ANN, we used The Split Port Ship Classification Dataset.