The 1 st Workshop on Maritime Computer Vision (MaCVi) 2023 focused on maritime computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicle (USV), and organized several subchallenges in this domain: (i) UAV-based Maritime Object Detection, (ii) UAV-based Mar-itime Object Tracking, (iii) USV-based Maritime Obstacle Segmentation and (iv) USV-based Maritime Obstacle Detection. The subchallenges were based on the SeaDronesSee and MODS benchmarks. This report summarizes the main findings of the individual subchallenges and introduces a new benchmark, called SeaDronesSee Object Detection v2, which extends the previous benchmark by including more classes and footage. We provide statistical and qualitative analyses, and assess trends in the best-performing method-ologies of over 130 submissions. The methods are sum-marized in the appendix. The datasets, evaluation code and the leaderboard are publicly available (https://seadronessee.cs.uni-tuebingen.de/macvi).

1st Workshop on Maritime Computer Vision (MaCVi) 2023: Challenge Results

Poiesi, Fabio;
2023-01-01

Abstract

The 1 st Workshop on Maritime Computer Vision (MaCVi) 2023 focused on maritime computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicle (USV), and organized several subchallenges in this domain: (i) UAV-based Maritime Object Detection, (ii) UAV-based Mar-itime Object Tracking, (iii) USV-based Maritime Obstacle Segmentation and (iv) USV-based Maritime Obstacle Detection. The subchallenges were based on the SeaDronesSee and MODS benchmarks. This report summarizes the main findings of the individual subchallenges and introduces a new benchmark, called SeaDronesSee Object Detection v2, which extends the previous benchmark by including more classes and footage. We provide statistical and qualitative analyses, and assess trends in the best-performing method-ologies of over 130 submissions. The methods are sum-marized in the appendix. The datasets, evaluation code and the leaderboard are publicly available (https://seadronessee.cs.uni-tuebingen.de/macvi).
2023
979-8-3503-2056-5
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11582/337047
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