Size-Invariant Detection of Marine Vessels From Visual Time Series

Tunai Porto Marques, Alexandra Branzan Albu, Patrick O'Hara, Norma Serra, Ben Morrow, Lauren McWhinnie, Rosaline Canessa; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2021, pp. 443-453


Marine vessel traffic is one of the main sources of negative anthropogenic impact upon marine environments. The automatic identification of boats in monitoring images facilitates conservation, research and patrolling efforts. However, the diverse sizes of vessels, the highly dynamic water surface and weather-related visibility issues significantly hinder this task. While recent deep learning (DL)-based object detectors identify well medium- and large-sized boats, smaller vessels, often responsible for substantial disturbance to sensitive marine life, are typically not detected. We propose a detection approach that combines state-of-the-art object detectors and a novel Detector of Small Marine Vessels (DSMV) to identify boats of any size. The DSMV uses a short time series of images and a novel bi-directional Gaussian Mixture technique to determine motion in combination with context-based filtering and a DL-based image classifier. Experimental results obtained on our publicly-released datasets of images containing boats of various sizes show that the proposed approach comfortably outperforms five popular state-of-the-art object detectors. Code and datasets available at

Related Material

@InProceedings{Marques_2021_WACV, author = {Marques, Tunai Porto and Albu, Alexandra Branzan and O'Hara, Patrick and Serra, Norma and Morrow, Ben and McWhinnie, Lauren and Canessa, Rosaline}, title = {Size-Invariant Detection of Marine Vessels From Visual Time Series}, booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)}, month = {January}, year = {2021}, pages = {443-453} }