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1st Workshop on Maritime Computer Vision 2023: Challenge Results

2022-11-24 09:59:13
Benjamin Kiefer, Matej Kristan, Janez Perš, Lojze Žust, Fabio Poiesi, Fabio Augusto de Alcantara Andrade, Alexandre Bernardino, Matthew Dawkins, Jenni Raitoharju, Yitong Quan, Adem Atmaca, Timon Höfer, Qiming Zhang, Yufei Xu, Jing Zhang, Dacheng Tao, Lars Sommer, Raphael Spraul, Hangyue Zhao, Hongpu Zhang, Yanyun Zhao, Jan Lukas Augustin, Eui-ik Jeon, Impyeong Lee, Luca Zedda, Andrea Loddo, Cecilia Di Ruberto, Sagar Verma, Siddharth Gupta, Shishir Muralidhara, Niharika Hegde, Daitao Xing, Nikolaos Evangeliou, Anthony Tzes, Vojtěch Bartl, Jakub Špaňhel, Adam Herout, Neelanjan Bhowmik, Toby P. Breckon, Shivanand Kundargi, Tejas Anvekar, Chaitra Desai, Ramesh Ashok Tabib, Uma Mudengudi, Arpita Vats, Yang Song, Delong Liu, Yonglin Li, Shuman Li, Chenhao Tan, et al. (23 additional authors not shown)

Abstract

The 1$^{\text{st}}$ 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 Maritime 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, which are (1) UAV-based Maritime Object Detection, (2) UAV-based Maritime Object Tracking, (3) USV-based Maritime Obstacle Segmentation and (4) USV-based Maritime Obstacle Detection. This report summarizes the main findings of the individual subchallenges and introduces %Furthermore, we introduce 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 methodologies of over 130 submissions. The methods are summarized in the appendix. %The tech report for most of the top performing methods is attached. The datasets, evaluation code and the %competition's final standing leaderboard are publicly available at this https URL.

Abstract (translated)

URL

https://arxiv.org/abs/2211.13508

PDF

https://arxiv.org/pdf/2211.13508.pdf


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