This paper discusses the technical challenges in maritime image processing and machine vision problems for video streams generated by cameras. Even well documented problems of horizon detection and registration of frames in a video are very challenging in maritime scenarios. More advanced problems of background subtraction and object detection in video streams are very challenging. Challenges arising from the dynamic nature of the background, unavailability of static cues, presence of small objects at distant backgrounds, illumination effects, all contribute to the challenges as discussed here.
Unmanned Aerial Vehicles (UAVs) are of crucial importance in search and rescue missions in maritime environments due to their flexible and fast operation capabilities. Modern computer vision algorithms are of great interest in aiding such missions. However, they are dependent on large amounts of real-case training data from UAVs, which is only available for traffic scenarios on land. Moreover, current object detection and tracking data sets only provide limited environmental information or none at all, neglecting a valuable source of information. Therefore, this paper introduces a large-scaled…
University of Tuebingen（图宾根大学）；University of Ljubljana（卢布尔雅那大学）；Fondazione Bruno Kessler（布鲁诺·凯塞勒基金会）；University of South-Eastern Norway（东南挪威大学）；University of Lisbon（里斯本大学）；Kitware（Kitware公司）；University of Jyväskylä（于韦斯屈莱大学）；The University of Sydney（悉尼大学）；JD Explore Academy（京东探索研究院）；Fraunhofer IOSB（弗劳恩霍夫光学、信息技术与电子学研究所）；Beijing University of Posts and Telecommunications（北京邮电大学）；Helmut Schmidt University（赫尔穆特·施密特大学）；University of Seoul（首尔大学）；University of Cagliari（卡利亚里大学）；Granular AI（Granular AI公司）；TU Kaiserslautern（凯泽斯劳滕工业大学）
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 and introduces a new benchmark, called SeaDronesSee…
The University of Western Australia（西澳大学）；The University of Melbourne（墨尔本大学）；Defence Science and Technology Group（国防科学技术集团）
Small object detection (SOD) in optical images and videos is a challenging problem that even state-of-the-art generic object detection methods fail to accurately localize and identify such objects. Typically, small objects appear in real-world due to large camera-object distance. Because small objects occupy only a small area in the input image (e.g., less than 10%), the information extracted from such a small area is not always rich enough to support decision making. Multidisciplinary strategies are being developed by researchers working at the interface of deep learning and computer vision t…
Misalignment between model predictions and intended usage can be detrimental for the deployment of computer vision models. The issue is exacerbated when the task involves complex structured outputs, as it becomes harder to design procedures which address this misalignment. In natural language processing, this is often addressed using reinforcement learning techniques that align models with a task reward. We adopt this approach and show its surprising effectiveness across multiple computer vision tasks, such as object detection, panoptic segmentation, colorization and image captioning. We belie…
School of Computing Technologies, RMIT University（皇家墨尔本理工大学计算技术学院）
In this report we analysed a possibility of using computer vision techniques for automated reading of utility meters. In our study, we focused on two computer vision techniques: an open-source solution Tensorflow Object Detection (Tensorflow) and a commercial solution Anyline. This report extends our previous publication: We start with presentation of a structured analysis of related approaches. After that we provide a detailed comparison of two computer vision technologies, Tensorflow Object Detection (Tensorflow) and Anyline, applied to semi-automated reading of utility meters. In this paper…
Towards a Better Understanding of the Computer Vision Research Community in Africa
University of California, Davis（加州大学戴维斯分校）；German University in Cairo（开罗德国大学）；Nile University（尼罗河大学）；University College London（伦敦大学学院）；University of Angers（昂热大学）；University of Sfax（斯法克斯大学）；Ashesi University（阿希西大学）
Humans can naturally and effectively find salient regions in complex scenes. Motivated by this observation, attention mechanisms were introduced into computer vision with the aim of imitating this aspect of the human visual system. Such an attention mechanism can be regarded as a dynamic weight adjustment process based on features of the input image. Attention mechanisms have achieved great success in many visual tasks, including image classification, object detection, semantic segmentation, video understanding, image generation, 3D vision, multi-modal tasks and self-supervised learning. In th…
Faculdade de Engenharia, Universidade do Porto（波尔图大学工程学院）；LIAAD - INESC TEC（LIAAD - 葡萄牙系统与计算机工程研究所）；FC Porto – Futebol SAD（波尔图足球俱乐部）
Computer Vision developments are enabling significant advances in many fields, including sports. Many applications built on top of Computer Vision technologies, such as tracking data, are nowadays essential for every top-level analyst, coach, and even player. In this paper, we survey Computer Vision techniques that can help many sports-related studies gather vast amounts of data, such as Object Detection and Pose Estimation. We provide a use case for such data: building a model for shot speed estimation with pose data obtained using only Computer Vision models. Our model achieves a correlation…
Deep reinforcement learning augments the reinforcement learning framework and utilizes the powerful representation of deep neural networks. Recent works have demonstrated the remarkable successes of deep reinforcement learning in various domains including finance, medicine, healthcare, video games, robotics, and computer vision. In this work, we provide a detailed review of recent and state-of-the-art research advances of deep reinforcement learning in computer vision. We start with comprehending the theories of deep learning, reinforcement learning, and deep reinforcement learning. We then pr…
The 2nd Workshop on Maritime Computer Vision (MaCVi) 2024
University of Tuebingen（蒂宾根大学）；University of Ljubljana（卢布尔雅那大学）；Luxonis（Luxonis公司）；Sentient Vision Systems（Sentient视觉系统公司）；Queensland University of Technology（昆士兰科技大学）；University of Washington（华盛顿大学）；Fraunhofer IOSB（弗劳恩霍夫IOSB研究所）；Fraunhofer Center for Machine Learning（弗劳恩霍夫机器学习中心）；Lenovo Research（联想研究院）；Lenovo（联想集团）；Fudan University（复旦大学）；University of Cagliari（卡利亚里大学）；The Hong Kong University of Science and Technology（香港科技大学）
Microsoft Cloud and AI（微软云与人工智能部门）；Microsoft Research Redmond（微软雷德蒙德研究院）
Automated visual understanding of our diverse and open world demands computer vision models to generalize well with minimal customization for specific tasks, similar to human vision. Computer vision foundation models, which are trained on diverse, large-scale dataset and can be adapted to a wide range of downstream tasks, are critical for this mission to solve real-world computer vision applications. While existing vision foundation models such as CLIP, ALIGN, and Wu Dao 2.0 focus mainly on mapping images and textual representations to a cross-modal shared representation, we introduce a new co…
University of Southern California（南加州大学）；Hong Kong Baptist University（香港浸会大学）；Tencent AI Lab（腾讯AI Lab）
Federated Learning (FL) is a distributed learning paradigm that can learn a global or personalized model from decentralized datasets on edge devices. However, in the computer vision domain, model performance in FL is far behind centralized training due to the lack of exploration in diverse tasks with a unified FL framework. FL has rarely been demonstrated effectively in advanced computer vision tasks such as object detection and image segmentation. To bridge the gap and facilitate the development of FL for computer vision tasks, in this work, we propose a federated learning library and benchma…
3rd Workshop on Maritime Computer Vision (MaCVi) 2025: Challenge Results
LOOKOUT；University of Tuebingen（蒂宾根大学）；University of Ljubljana（卢布尔雅那大学）；Luxonis（Luxonis公司）；Shield AI（Shield AI公司）；Queensland University of Technology（昆士兰科技大学）；Center of Excellence in Visual Intelligence, KLE Technological University（KLE科技大学视觉智能卓越中心）；Nanjing University of Science and Technology（南京理工大学）；University of Zagreb Faculty of Electrical Engineering and Computing（萨格勒布大学）；Gwangju Institute of Science and Technology (GIST)（光州科学技术院）；University of Liverpool（利物浦大学）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Xi’an Jiaotong-Liverpool University（西交利物浦大学）；Dalian Maritime University（大连海事大学）；National Cheng Kung University（国立成功大学）；Fraunhofer IOSB（弗劳恩霍夫IOSB研究所）；Yancheng Institute of Technology（盐城工学院）；Nanjing University（南京大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；Michigan Technological University（密歇根理工大学）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）
Domain Adaptation from Generated Multi-Weather Images for Unsupervised Maritime Object Classification
The School of Electrical and Information Engineering, Tianjin University, China（天津大学电气与信息工程学院）；Tiandy Technologies Co., Ltd, Tianjin, China（天津天盾科技有限公司）
Computer Vision-Based Early Detection of Container Loss at Sea
Centre of Excellence in Maritime Safety（海运安全卓越中心）；Singapore Polytechnic（新加坡理工学院）；Quality, Safety, Security and Environment（质量、安全、安保与环境）；Pacific International Lines (Pvt) Ltd（太平洋国际航运有限公司）
4th Workshop on Maritime Computer Vision (MaCVi): Challenge Overview
LOOKOUT；Helmut Schmidt University（海姆·施密特大学）；catskill GmbH（catskill公司）；University of Ljubljana（卢布尔雅那大学）；Virginia Tech（弗吉尼亚理工大学）；Shield AI；Queensland University of Technology（昆士兰理工大学）；Dartmouth College（达特茅斯学院）；LinkedIn；University of Tuebingen（图宾根大学）；Konya Technical University（科尼亚技术大学）；Schneider Electric Taiwan Co., Ltd.（施耐德电气台湾有限公司）；University of Taipei（台北大学）；National Yang Ming Chiao Tung University（阳明交通大学）；University at Albany, SUNY（阿尔巴尼大学，SUNY）；Inventec Corporation（Inventec公司）；HD Korea Shipbuilding & Offshore Engineering Co., Ltd.（韩国海事造船与海洋工程公司）；Seoul National University（首尔国立大学）；Xidian University（西安电子科技大学）；Indian Institute of Technology, Tirupati（印度泰浦蒂理工学院）；Centre for Artificial Intelligence and Robotics (CAIR), Bangalore, India（人工智能与机器人中心（CAIR），印度班加罗尔）；Gwangju Institute of Science and Technology（全州科学技术院）；Fraunhofer IOSB（弗劳恩霍夫 IOSB研究所）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；Kansas State University（堪萨斯州立大学）；Colorado School of Mines（科罗拉多矿业学院）；Arquimea Research Center（Arquimea研究中心）；Independent Researcher（独立研究员）
Computer vision has been thriving since AI development was gaining thrust. Using deep learning techniques has been the most popular way which computer scientists thought the solution of. However, deep learning techniques tend to show lower performance than manual processing. Using deep learning is not always the answer to a problem related to computer vision.
UC San Diego（加州大学圣地亚哥分校）；ATG Robotics（ATG机器人公司）；University of Bristol（布里斯托尔大学）；Aristotle University of Thessaloniki（塞萨洛尼基亚里士多德大学）
As an important component of autonomous systems, autonomous car perception has had a big leap with recent advances in parallel computing architectures. With the use of tiny but full-feature embedded supercomputers, computer stereo vision has been prevalently applied in autonomous cars for depth perception. The two key aspects of computer stereo vision are speed and accuracy. They are both desirable but conflicting properties, as the algorithms with better disparity accuracy usually have higher computational complexity. Therefore, the main aim of developing a computer stereo vision algorithm fo…
Vanderbilt University（范德堡大学）；Vanderbilt University Medical Center（范德堡大学医学中心）；Siemens Healthineers（西门子医疗）
Although deep learning prediction models have been successful in the discrimination of different classes, they can often suffer from poor calibration across challenging domains including healthcare. Moreover, the long-tail distribution poses great challenges in deep learning classification problems including clinical disease prediction. There are approaches proposed recently to calibrate deep prediction in computer vision, but there are no studies found to demonstrate how the representative models work in different challenging contexts. In this paper, we bridge the confidence calibration from …
Faculty of Electrical Engineering, Computer Science and Information Technology Osijek（奥西耶克电气工程、计算机科学与信息技术学院）
Visually impaired persons have significant problems in their everyday movement. Therefore, some of our previous work involves computer vision in developing assistance systems for guiding the visually impaired in critical situations. Some of those situations includes crosswalks on road crossings and stairs in indoor and outdoor environment. This paper presents an evaluation framework for computer vision-based guiding of the visually impaired persons in such critical situations. Presented framework includes the interface for labeling and storing referent human decisions for guiding directions an…
