2022-06-23 The Boat-MNIST challenge is over. Congratulations to the winner groups 10, 106, 120, 50 and 107! Your prizes are on here (if you manage to click). We will reopen the uploading option for this track soon.

2022-05-28 We created a Github repository for this benchmark. Over time, you will find code examples, baseline models and other helpful stuff over there.

2021-12-15 The SeaDronesSee evaluation webpage is online. IF you find any bugs, please send us an email. Thank you!


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 data set and leaderboard.

Object Detection: 5,630 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

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 the following paper:

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},
year={2022} }