Classification and recognition of spatio-temporal behavior of ships based on deep learning of trajectory feature images

Objective To address the issues of low efficiency and inaccurate ship behavior recognition when handling large-scale ship trajectory data, a method for recognizing and classifying ship behaviour based on trajectory feature image modelling and deep learning is proposed.

Method This research constructs a visual coding model for the salient features, including speed, acceleration, heading, steering rate and trajectory point density. It also realizes sample generation and enhancement processing of ship trajectory feature images. All of these are done while taking into account the multi-scale features of ship trajectory.

Results Based on the trajectory feature images, the deep learning model significantly improves the quality and accuracy of ship behavior recognition, with a recall rate of 90.99%, precision rate of 91.23%, and F1 score of 91.11%, which translates to an accuracy rate of 91.22%.The experimental results indicate that the speed, steering rate, and trajectory point density are the best feature combinations to distinguish the eight behaviors, such as straight ahead, steering, maneuvering loitering, berthing, and anchoring.

Conclusion The approach can successfully detect ship behaviors at various trajectory data scales, perform automatic ship behavior categorization and identification, and produce outcomes that may help make decisions for intelligent water traffic control.