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The 2nd Workshop on Maritime Computer Vision (MaCVi) 2024

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arxiv 2311.14762 v1 pith:UKHZ4HLW submitted 2023-11-23 cs.CV cs.AI

Benjamin Kiefer , Lojze Žust , Matej Kristan , Janez Perš , Matija Teršek , Arnold Wiliem , Martin Messmer , Cheng-Yen Yang
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Hsiang-Wei Huang Zhongyu Jiang Heng-Cheng Kuo Jie Mei Jenq-Neng Hwang Daniel Stadler Lars Sommer Kaer Huang Aiguo Zheng Weitu Chong Kanokphan Lertniphonphan Jun Xie Feng Chen Jian Li Zhepeng Wang Luca Zedda Andrea Loddo Cecilia Di Ruberto Tuan-Anh Vu Hai Nguyen-Truong Tan-Sang Ha Quan-Dung Pham Sai-Kit Yeung Yuan Feng Nguyen Thanh Thien Lixin Tian Sheng-Yao Kuan Yuan-Hao Ho Angel Bueno Rodriguez Borja Carrillo-Perez Alexander Klein Antje Alex Yannik Steiniger Felix Sattler Edgardo Solano-Carrillo Matej Fabijanić Magdalena Šumunec Nadir Kapetanović Andreas Michel Wolfgang Gross Martin Weinmann
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classification cs.CVcs.AI
keywords maritimecomputermacviusv-basedvisionworkshopchallengesdetection
verification ladder T0 review T1 audit T2 compute T3 formal
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The 2nd Workshop on Maritime Computer Vision (MaCVi) 2024 addresses maritime computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicles (USV). Three challenges categories are considered: (i) UAV-based Maritime Object Tracking with Re-identification, (ii) USV-based Maritime Obstacle Segmentation and Detection, (iii) USV-based Maritime Boat Tracking. The USV-based Maritime Obstacle Segmentation and Detection features three sub-challenges, including a new embedded challenge addressing efficicent inference on real-world embedded devices. This report offers a comprehensive overview of the findings from the challenges. We provide both statistical and qualitative analyses, evaluating trends from over 195 submissions. All datasets, evaluation code, and the leaderboard are available to the public at https://macvi.org/workshop/macvi24.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Real-Time Fusion of Visual and Chart Data for Enhanced Maritime Vision

    cs.CV 2025-07 conditional novelty 6.0 of 10

    A chart-conditioned transformer associates visible buoys with nautical chart markers end-to-end, outperforming projection and distance-estimation baselines on a single test video.

  2. Lightweight Multi-Frame Integration for Robust YOLO Object Detection in Videos

    cs.CV 2025-06 conditional novelty 5.0 of 10

    Multi-frame early fusion with single-frame supervision improves YOLOv7-tiny detection on MOT20Det and a new BOAT360 fisheye dataset.

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