Deep Learning-Empowered Unsupervised Maritime Anomaly Detection

Automatically detecting anomalous vessel behaviour is an extremely crucial problem in intelligent maritime surveillance. In this paper, a deep learning-based unsupervised method is proposed for detecting anomalies in vessel trajectories, operating at both the image and pixel levels. The original trajectory data is converted into a two-dimensional matrix representation to generate a vessel trajectory image. A wasserstein generative adversarial network (WGAN) model is trained on a dataset of normal vessel trajectories, while simultaneously training an encoder to map the trajectory image to a latent space. During anomaly detection, the vessel trajectory image is mapped to a hidden vector by the encoder, which is then used by the generator to reconstruct the input image. The anomaly score is computed based on the residuals between the reconstructed trajectory image and the discriminator’s residuals, enabling image-level anomaly detection. Furthermore, pixel-level anomaly detection is achieved by analyzing the residuals of the reconstructed image pixels to localize the anomalous trajectory. The proposed method is compared to autoencoder (AE) and variational autoencoder (VAE) model, and experimental results demonstrate its superior performance in anomaly detection and pixel-level localization. This method has substantial potential for detecting anomalies in vessel trajectories, as it can detect anomalies in arbitrary waters without prior knowledge, relying solely on training with normal vessel trajectories. This approach significantly reduces the need for human and material resources. Moreover, it provides valuable insights and references for trajectory anomaly detection in other domains, holding both theoretical and practical importance.