In this work a novel ships dataset is proposed consisting of more than 56k images of marine vessels collected by means of web-scraping and including 12 ship categories. A YOLOv3 single-stage detector based on Keras API is built on top of this dataset. Current results on four categories (cargo ship, naval ship, oil ship and tug ship) show Average Precision up to 96% for Intersection over Union (IoU) of 0.5 and satisfactory detection performances up to IoU of 0.8. A Data Analytics GUI service based on QT framework and Darknet-53 engine is also implemented in order to simplify the deployment process and analyse massive amount of images even for people without Data Science expertise.
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）
CmIVTP: Cross-modal Interaction-based Vessel Trajectory Prediction for Maritime Intelligence
Department of Logistics and Maritime Studies, the Hong Kong Polytechnic University（物流及海运研究系，香港理工大学）；Research Centre for ESG Advancement (RCESGA), the Hong Kong Polytechnic University（ESG进步研究中心（RCESGA），香港理工大学）；School of Navigation, Wuhan University of Technology（航海学院，武汉理工大学）
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）
The automatic identification system (AIS) and video cameras have been widely exploited for vessel traffic surveillance in inland waterways. The AIS data could provide the vessel identity and dynamic information on vessel position and movements. In contrast, the video data could describe the visual appearances of moving vessels, but without knowing the information on identity, position and movements, etc. To further improve vessel traffic surveillance, it becomes necessary to fuse the AIS and video data to simultaneously capture the visual features, identity and dynamic information for the vess…
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…
Centre Borelli, ENS Paris-Saclay（巴黎萨克雷高等师范学校博雷利中心）；iXblue（iXblue公司）
Maritime domain is one of the most challenging scenarios for object detection due to the complexity of the observed scene. In this article, we present a new approach to detect unidentified floating objects in the maritime environment. The proposed approach is capable of detecting floating objects without any prior knowledge of their visual appearance, shape or location. The input image from the video stream is denoised using a visual dictionary learned from a K-SVD algorithm. The denoised image is made of self-similar content. Later, we extract the residual image, which is the difference betwe…
WUTDet: A 100K-Scale Ship Detection Dataset and Benchmarks with Dense Small Objects
School of Navigation, Wuhan University of Technology（武汉理工大学航运学院）；Sanya Science and Education Innovation Park, Wuhan University of Technology（武汉理工大学三亚科教创新园）；School of Electronic Information and Communications, Huazhong University of Science and Technology（华中科技大学电子信息与通信学院）；School of Electrical and Information Engineering, Tianjin University（天津大学电气与信息工程学院）
Adversarial multi-task underwater acoustic target recognition: towards robustness against various influential factors
Key Laboratory of Speech Acoustics and Content Understanding, Institute of Acoustics, Chinese Academy of Sciences（中国科学院声学研究所语言声学与内容理解重点实验室）；University of Chinese Academy of Sciences（中国科学院大学）；State Key Laboratory of Acoustics, Institute of Acoustics, Chinese Academy of Sciences（中国科学院声学研究所声场声信息国家重点实验室）
FeedbackSTS-Det: Sparse Frames-Based Spatio-Temporal Semantic Feedback Network for Moving Infrared Small Target Detection
School of Information and Communication Engineering and the Laboratory of Imaging Detection and Intelligent Perception, University of Electronic Science and Technology of China（电子科技大学信息与通信工程学院及成像探测与智能感知实验室）；School of Television and Audiovisual Arts, Communication University of Zhejiang（浙江传媒学院电视与视听艺术学院）
Geopositioning and tracking a moving boat at sea is a very challenging problem, requiring boat detection, matching and estimating its GPS location from imagery with no common features. The problem can be stated as follows: given imagery from a camera mounted on a moving platform with known GPS location as the only valid sensor, we predict the geoposition of a target boat visible in images. Our solution uses recent ML algorithms, the camera-scene geometry and Bayesian filtering. The proposed pipeline first detects and tracks the target boat's location in the image with the strategy of tracking …
Unified Restoration-Perception Learning: Maritime Infrared-Visible Image Fusion and Segmentation
Laboratory of Underwater Acoustic Communication and Marine Information Technology, Ministry of Education, Xiamen University（厦门大学水声通信与海洋信息技术教育部重点实验室）；PAMI Research Group, Department of Computer and Information Science, University of Macau（澳门大学计算机与信息科学系PAMI研究组）；School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区网络空间安全学院）
Modulation Feature Enhancement with a Multi-Stage Attention Network for Underwater Acoustic Target Recognition
Institute of Acoustics, Chinese Academy of Sciences（中国科学院声学研究所）；University of Chinese Academy of Sciences（中国科学院大学）；School of Computer Science and Electronics Engineering, University of Essex（埃塞克斯大学计算机科学与电子工程学院）
KSG-Net: Key-Sparse and Global-Context Learning for Maritime 3D Ship Detection
School of Computer Science and Technology, Wuhan University of Science and Technology（武汉科技大学计算机科学与技术学院）；State Key Laboratory of Robotics and Intelligent Systems, Shenyang Institute of Automation, Chinese Academy of Sciences（中国科学院沈阳自动化研究所机器人与智能系统国家重点实验室）；China University of Chinese Academy of Sciences（中国科学院大学）；Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System, Wuhan University of Science and Technology（武汉科技大学智能信息处理与实时工业系统湖北省重点实验室）
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…
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）
There is an increasing demand for software that automatically detects and classifies mobile targets such as airplanes, cars, and ships in satellite imagery. Applications of such automated target recognition (ATR) software include economic forecasting, traffic planning, maritime law enforcement, and disaster response. This paper describes the extension of a convolutional neural network (CNN) for classification to a sliding window algorithm for detection. It is evaluated on mobile targets of the xView dataset, on which it achieves detection and classification accuracies higher than 95%.
McMaster University（麦克马斯特大学）；Defence Research and Development Canada（加拿大国防研究与发展部）
Recognition and identification of unknown targets is a crucial task in surveillance and security systems. Electronic Support Measures (ESM) are one of the most effective sensors for identification, especially for maritime and air--to--ground applications. In typical surveillance systems multiple ESM sensors are usually deployed along with kinematic sensors like radar. Different ESM sensors may produce different types of reports ready to be sent to the fusion center. The focus of this paper is to develop a new architecture for target recognition and identification when non--homogeneous ESM and …
RPCASSM: Robust PCA State Space Model For Infrared Small Target Detection
College of Computer Science and Technology, Jilin University（吉林大学计算机科学与技术学院）；Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University（教育部符号计算与知识工程重点实验室）；College of Software, Jilin University（吉林大学软件学院）；School of Geosciences, Yangtze University（长江大学地球科学学院）；College of Communication Engineering, Jilin University（吉林大学通信工程学院）
MoMuSE: Momentum Multi-modal Target Speaker Extraction for Real-time Scenarios with Impaired Visual Cues
The Hong Kong Polytechnic University（香港理工大学）；Shenzhen Research Institute of Big Data（深圳大数据研究院）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））
