The detection of anomalies is crucial to ensuring the safety and security of maritime vessel traffic surveillance. Although autoencoders are popular for anomaly detection, their effectiveness in identifying collective and contextual anomalies is limited, especially in the maritime domain, where anomalies depend on vessel-specific contexts derived from self-reported AIS messages. To address these limitations, we propose a novel solution: the context-aware autoencoder. By integrating context-specific thresholds, our method improves detection accuracy and reduces computational cost. We compare four context-aware autoencoder variants and a conventional autoencoder using a case study focused on fishing status anomalies in maritime surveillance. Results demonstrate the significant impact of context on reconstruction loss and anomaly detection. The context-aware autoencoder outperforms others in detecting anomalies in time series data. By incorporating context-specific thresholds and recognizing the importance of context in anomaly detection, our approach offers a promising solution to improve accuracy in maritime vessel traffic surveillance systems.
Multi-Modal Graph Neural Network-Inspired Vessel Identification and Tracking for UAV-Based Maritime Surveillance
Wuhan University of Technology（武汉理工大学）；University of Aberdeen（阿伯丁大学）；Guangdong University of Technology（广东工业大学）；The Hong Kong Polytechnic University（香港理工大学）
Asynchronous Trajectory Matching-Based Multimodal Maritime Data Fusion for Vessel Traffic Surveillance in Inland Waterways
Wuhan University of Technology（武汉理工大学）；National Engineering Research Center for Water Transport Safety（国家水运安全工程技术研究中心）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
Multi-Task Learning-Enabled Automatic Vessel Draft Reading for Intelligent Maritime Surveillance
Wuhan University of Technology（武汉理工大学）；National Taiwan University of Science and Technology（台湾科技大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
Bridging the Gap Between Image Restoration and Navigational Safety in Hazy Conditions: A New Visibility Estimation Metric for Maritime Surveillance
Sanya Science and Education Innovation Park, Wuhan University of Technology, Sanya 572000, China（武汉理工大学三亚科学教育创新园，三亚572000，中国）；School of Navigation, Wuhan University of Technology, Wuhan 430063, China（武汉理工大学航海学院，武汉430063，中国）；State Key Laboratory of Maritime Technology and Safety, Wuhan University of Technology, Wuhan 430063, China（武汉理工大学海洋技术与安全国家重点实验室，武汉430063，中国）；Department of Mechanics and Maritime Sciences, Chalmers University of Technology, Göteborg 41296, Sweden（楚科奇大学机械与航海科学系，哥德堡41296，瑞典）
Neptune-X: Active X-to-Maritime Generation for Universal Maritime Object Detection
Hong Kong JC STEM Lab of Smart City and Department of Computer Science, City University of Hong Kong（香港智慧城市JC STEM实验室及计算机科学系，香港城市大学）；Singapore Management University（新加坡管理大学）；State Key Laboratory of Maritime Technology and Safety, Wuhan University of Technology（船舶技术与安全国家重点实验室，武汉理工大学）；The Hong Kong Polytechnic University（香港理工大学）；Wuhan University of Science and Technology（武汉科技大学）
Bidirectional skip-frame prediction for video anomaly detection with intra-domain disparity-driven attention
School of Computer Science and Engineering, Xi'an University of Technology, Xi'an, China（西安理工大学计算机科学与工程学院）；CCTEG Changzhou Research Institute（中技集团常州研究院；天迪（常州）自动化有限公司）；Tiandi(Changzhou) Automation Co., Ltd.（西安理工大学计算机科学与技术学院）；College of Computer Science and Technology, Xi'an University of Science and Technology
Video Anomaly Detection Using Pre-Trained Deep Convolutional Neural Nets and Context Mining
NSF Center for Cloud and Autonomic Computing（NSF云与自主计算中心）；Department of Computer Science（计算机科学系）；The University of Arizona（亚利桑那大学）；The University of North Texas（北得克萨斯大学）
Deep Learning-Based Object Detection in Maritime Unmanned Aerial Vehicle Imagery: Review and Experimental Comparisons
Wuhan University of Technology（武汉理工大学）；Chongqing Research Institute, Wuhan University of Technology（武汉理工大学重庆研究院）；Nanyang Technological University（南洋理工大学）
VRAE: Vertical Residual Autoencoder for License Plate Denoising and Deblurring
Ha Noi University of Science and Technology（河内科学技术大学）；Center of Environmental Intelligence（环境智能中心）；VinUniversity（文大学）；University of Southern California（南加州大学）
DMSORT: An efficient parallel maritime multi-object tracking architecture for unmanned vessel platforms
College of Information Science and Engineering, Ocean University of China（信息科学与工程学院，中国海洋大学）；Marine Engineering College, Dalian Maritime University（航海工程学院，大连海事大学）；CSSC Haiwei Tech Co., Ltd（CSSC海威科技有限公司）；College of Artificial Intelligence, Dalian Maritime University（人工智能学院，大连海事大学）；College of Marine Electrical Engineering, Dalian Maritime University（海洋电气工程学院，大连海事大学）
MODS -- A USV-oriented object detection and obstacle segmentation benchmark
University of Ljubljana, Faculty of Computer and Information Science（卢布尔雅那大学计算机与信息科学学院）；University of Ljubljana, Faculty of Electrical Engineering（卢布尔雅那大学电气工程学院）；MPRI；Sirio d.o.o.（Sirio有限责任公司）
Automatized marine vessel monitoring from sentinel-1 data using convolution neural network
Center for Environment & Water, Research Institute, King Fahd University of Petroleum & Minerals（法赫德国王石油与矿产大学环境与水研究中心）；University of Petroleum and Energy Studies（石油与能源研究大学）；Dr. B.C. Roy Engineering College（B.C.罗伊博士工程学院）；CubicX
针对传统 CFAR 船舶检测缺乏决策能力的问题，提出基于小波变换的卷积神经网络，利用 Sentinel-1 双极化 SAR 数据实现印度西海岸繁忙交通场景下的船舶自动识别，检测准确率达 95.46%。
