SeaDronesSee is a large-scale data set aimed at helping develop systems for Search and Rescue (SAR) using Unmanned Aerial Vehicles (UAVs) in maritime scenarios. Building highly complex autonomous UAV/drone systems that aid in SAR missions requires robust computer vision algorithms to detect and track objects or persons of interest. This data set provides three sets of tracks: object detection, single-object tracking and multi-object tracking. Each track consists of its own fully labeled data set and for most there is a leaderboard.
Item | Start Time |
---|---|
Opening | 08:30 |
1st Keynote: Underwater computer vision challenges | 08:40 |
2nd Keynote: UAV-based object detection for maritime Search-And-Rescue missions     | 09:10 |
Spotlight presentations of submitted papers     | 09:40 |
Coffee break & poster session; simultaneous online meet-up | 10:20 |
3rd Keynote: Satellite-based marine litter detection     | 10:45 |
Challenges overview and results | 11:15 |
Presentations of challenge winners | 12:15 |
5 minute break | 12:55 |
4th Keynote: Scaling aerial whale monitoring using active learning | 13:00 |
Panel Discussion with Sentient-Vision, Whaleseeker, and TBA. | 13:30 |
Closing Remarks | 14:00 |
Object Detection v2: 8,930 train images, 1,547 validation images, 3,750 testing images
Object Detection: 2,975 train images, 859 validation images, 1,796 testing images
Single-Object Tracking: 58 training video clips, 70 validation video clips and 80 testing video clips
Multi-Object Tracking: 22 video clips with 54,105 frames
Multi-Spektral Object Detection: 246 train images, 61 validation images, 125 testing images
MODS Obstacle Detection and Segmentation: hosted as part of the upcoming Workshop.
DeepGTAV-SeaDronesSee: 90,000 synthetic images
Seagull - Traffic Monitoring and Surveillance: advertised here as part of the upcoming Workshop.
Boat-MNIST: 3,765 train images, 1,506 validation images, 2,259 testing images
We will continue to update this data set to make it more versatile and reflect real-world requirements in dynamic situations.
If you find SeaDronesSee or this evaluation webpage useful, consider citing this or this paper:
@article{kiefer20221st, title={1st Workshop on Maritime Computer Vision (MaCVi) 2023: Challenge Results}, author={Kiefer, Benjamin and Kristan, Matej and Per{\v{s}}, Janez and {\v{Z}}ust, Lojze and Poiesi, Fabio and Andrade, Fabio Augusto de Alcantara and Bernardino, Alexandre and Dawkins, Matthew and Raitoharju, Jenni and Quan, Yitong and others}, journal={arXiv preprint arXiv:2211.13508}, year={2022} }
@inproceedings{varga2022seadronessee,
title={Seadronessee: A maritime benchmark for detecting humans in open water},
author={Varga, Leon Amadeus and Kiefer, Benjamin and Messmer, Martin and Zell, Andreas},
booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
pages={2260--2270},
year={2022}
}
If you use MODS, consider citing the following paper:
@article{bovcon2021mods,
title={MODS--A USV-oriented object detection and obstacle segmentation benchmark},
author={Bovcon, Borja and Muhovi{\v{c}}, Jon and Vranac, Du{\v{s}}ko and Mozeti{\v{c}}, Dean and Per{\v{s}}, Janez and Kristan, Matej},
journal={IEEE Transactions on Intelligent Transportation Systems},
year={2021},
publisher={IEEE}
}