SeePerSea: Multi-modal Perception Dataset of In-water Objects for Autonomous Surface Vehicles
This dataset addresses the research gap in autonomous surface vehicles by providing a multi-modal, annotated dataset for object detection and classification, which is incremental as it builds on existing perception methods.
The paper introduces SeePerSea, the first publicly accessible labeled multi-modal perception dataset for autonomous maritime navigation, collected over 4 years with diverse in-water objects under varying conditions, and demonstrates its applicability by training deep learning-based perception algorithms.
This paper introduces the first publicly accessible labeled multi-modal perception dataset for autonomous maritime navigation, focusing on in-water obstacles within the aquatic environment to enhance situational awareness for Autonomous Surface Vehicles (ASVs). This dataset, collected over 4 years and consisting of diverse objects encountered under varying environmental conditions, aims to bridge the research gap in autonomous surface vehicles by providing a multi-modal, annotated, and ego-centric perception dataset, for object detection and classification. We also show the applicability of the proposed dataset by training deep learning-based open-source perception algorithms that have shown success. We expect that our dataset will contribute to development of the marine autonomy pipelines and marine (field) robotics. This dataset is opensource and can be found at https://seepersea.github.io/.