Learning Maritime Obstacle Detection From Weak Annotations by Scaffolding

Lojze ┼Żust, Matej Kristan; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2022, pp. 955-964

Abstract


Coastal water autonomous boats rely on robust perception methods for obstacle detection and timely collision avoidance. The current state-of-the-art is based on deep segmentation networks trained on large datasets. Per-pixel ground truth labeling of such datasets, however, is labor-intensive and expensive. We observe that far less information is required for practical obstacle avoidance -- the location of water edge on static obstacles like shore and approximate location and bounds of dynamic obstacles in the water is sufficient to plan a reaction. We propose a new scaffolding learning regime (SLR) that allows training obstacle detection segmentation networks only from such weak annotations, thus significantly reducing the cost of ground-truth labeling. Experiments show that maritime obstacle segmentation networks trained using SLR substantially outperform the same networks trained with dense ground truth labels, despite a significant reduction in labelling effort. Thus accuracy is not sacrificed for labelling simplicity but is in fact improved, which is a remarkable result.

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[bibtex]
@InProceedings{Zust_2022_WACV, author = {\v{Z}ust, Lojze and Kristan, Matej}, title = {Learning Maritime Obstacle Detection From Weak Annotations by Scaffolding}, booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)}, month = {January}, year = {2022}, pages = {955-964} }