A method for detecting abnormal behavior of ships based on multi-dimensional density distance and an abnormal isolation mechanism

Abnormal ship behavior detection is essential for maritime navigation safety. Most existing abnormal ship behavior detection methods only build A ship trajectory position outlier detection model; however, the construction of a ship speed outlier detection model is also significant for maritime navigation safety. In addition, in most existing methods for detecting a ship's abnormal behavior based on abnormal thresholds, one unsuitable threshold leads to the risk of the ship not being minimized as much as possible. In this paper, we proposed an abnormal ship behavior detection method based on distance measurement and an isolation mechanism. First, to address the problem of traditional trajectory compression methods and density clustering methods only using ship position information, the minimum description length principle based on acceleration (AMDL) algorithm and Multi-Dimensional Density Clustering (MDDBSCAN) algorithm is used in this study. These algorithms not only considered the position information of the ship, but also the speed information. Second, regarding the issue of the difficulty in determining the anomaly threshold, one method for determining the anomaly threshold based on the relationship between the velocity weights and noise points of the MDDBSCAN algorithm has been introduced. Finally, due to the randomness issue of the selected segmentation value in iForest, a strategy of selectively constructing isolated trees was proposed, thus further improving the efficiency of abnormal ship behavior detection. The experimental results on the historical automatic identification system data set of Xiamen port prove the practicality and effectiveness of our proposed method. Our experiment results show that the proposed method achieves an improvement of about 10% over the trajectory outlier detection based on the local outlier fraction method, about 14% over the isolation-based online anomalous trajectory method in terms of the accuracy of ship position information anomaly detection, and about 3% over the feature fusion method in terms of the accuracy of ship speed anomaly detection. This method improves algorithm efficiency by about 5% compared to the traditional isolation forest anomaly detection algorithm

Research and Improvement of Offshore Ship Fusion Recognition Algorithm

[Introduction] At present in China, the ships near offshore wind power platforms are mainly monitored by means of the ship AIS system and remote cameras. Such means lacking information technology often require a lot of manpower and material resources. In order to effectively warn the ships near the offshore wind power platform, this paper analyzes the urgent problems to be solved that are encountered in the current offshore ship identification, and proposes an offshore ship fusion recognition algorithm that combines the improved Faster-RCNN network and ship AIS system. [Method] Firstly, improvement suggestions were proposed for three aspects of the Fast-RCNN model, and the structures such as the backbone network and the loss function were adjusted. Secondly, the ships in the pictures taken by the remote cameras were detected by the improved Faster-RCNN network, and the results were supplemented and corrected in combination with the relevant information from the ship AIS system. Finally, the verification sets were tested according to the optimal model saved in the model training process, and each model was evaluated using the indicators of precision, recall and average precision. [Result] The Faster-RCNN model inference speed and accuracy for different feature extraction networks and classification loss functions are improved greatly. The ability of offshore wind power platforms to monitor ships is improved. The offshore ship information was processed and the navigation trajectory was obtained in combination with the ship AIS system, realizing the detection of the ships in the pictures taken by the remote cameras. [Conclusion] Experiments show that the feature extraction network and the replacement of the classification loss function of the traditional Faster-RCNN can effectively improve the detection accuracy of the network in the ship recognition task and the ship trajectory can be effectively obtained by integrating the ship AIS system

Reliable Navigational Scene Perception for Autonomous Ships in Maritime Environment

Due to significant advances in robotics and transportation, research on autonomous ships has attracted considerable attention. The most critical task is to make the ships capable of accurately, reliably, and intelligently detecting their surroundings to achieve high levels of autonomy. Three deep learning-based models are constructed in this thesis to perform complex perceptual tasks such as identifying ships, analysing encounter situations, and recognising water surface objects. In this thesis, sensors, including the Automatic Identification System (AIS) and cameras, provide critical information for scene perception. Specifically, the AIS enables mid-range and long-range detection, assisting the decision-making system to take suitable and decisive action. A Convolutional Neural Network-Ship Movement Modes Classification (CNN-SMMC) is used to detect ships or objects. Following that, a Semi- Supervised Convolutional Encoder-Decoder Network (SCEDN) is developed to classify ship encounter situations and make a collision avoidance plan for the moving ships or objects. Additionally, cameras are used to detect short-range objects, a supplementary solution to ships or objects not equipped with an AIS. A Water Obstacle Detection Network based on Image Segmentation (WODIS) is developed to find potential threat targets. A series of quantifiable experiments have demonstrated that these models can provide reliable scene perception for autonomous ships

Statistical tests for a ship detector based on the Polarimetric Notch Filter

Ship detection is an important topic in remote sensing and Synthetic Aperture Radar has a valuable contribution, allowing detection at night time and with almost any weather conditions. Additionally, polarimetry can play a significant role considering its capability to discriminate between different targets. Recently, a new ship detector exploiting polarimetric information was developed, namely the Geometrical Perturbation Polarimetric Notch Filter (GP-PNF). This work is focused on devising two statistical tests for the GP-PNF. The latter allow an automatic and adaptive selection of the detector threshold. Initially, the probability density function (pdf) of the detector is analytically derived. Finally, the Neyman-Pearson (NP) lemma is exploited to set the threshold calculating probabilities using the clutter pdf (i.e. a Constant False Alarm Rate, CFAR) and a likelihood ratio (LR). The goodness of fit of the clutter pdf is tested with four real SAR datasets acquired by the RADARSAT-2 and the TanDEM-X satellites. The former images are quad-polarimetric, while the latter are dual-polarimetric HH/VV. The data are accompanied by the Automatic Identification System (AIS) location of vessels, which facilitates the validation of the detection masks. It can be observed that the pdf's fit the data histograms and they pass the two sample Kolmogorov-Smirnov and χ2 tests

Automatic waterline registration of a ship model in waves

C

Workflow to detect ship encounters at sea with GIS support

Dissertation presented as the partial requirement for obtaining a Master's degree in Geographic Information Systems and ScienceAccording to the United Nations, more than 80% of the global trade is currently transported by sea. The Portuguese EEZ has a very extensive area with high maritime traffic, among which illicit activities may occur. This work aims to contribute to the official control of illegal transshipment actions by studying and proposing a new way of detecting encounters between ships. Ships with specific characteristics use an Automatic Identification System (AIS) on board which transmits a signal via radio frequencies, allowing shore stations to receive static and dynamic data from the ship. Thus, there is an increase in maritime situational awareness and, consequently, in the safety of navigation. The methodology of this dissertation employs monthly and daily AIS data in the study area, which is located in southern mainland Portugal. A bibliometric and content analysis was performed in order to assess the state of the art concerning geospatial analysis models of maritime traffic, based on AIS data, and focus on anomalous behaviour detection. Maritime traffic density maps were created with the support of a GIS (QGIS software), which allowed to characterize the maritime traffic in the study area and, subsequently, to pattern the locations where ship encounters occur. The algorithm to detect ship-to-ship meetings at sea was developed using a rule-based methodology. After analysis and discussion of results, it was found that the areas where the possibility of ship encounters at sea is greatest are away from the main shipping lanes, but close to areas with fishing vessels. The study findings and workflow are useful for decision making by the competent authorities for patrolling the maritime areas, focusing on the detection of illegal transhipment actions.Segundo as Nações Unidas, mais de 80% do comércio global é, atualmente, transportado por via marítima. A ZEE portuguesa tem uma área muito extensa, com tráfego marítimo elevado, entre o qual podem ocorrer atividades ilícitas. Este trabalho pretende contribuir para o controlo oficial de ações de transbordo ilegal, estudando e propondo uma nova forma de deteção de encontros entre navios. Os navios com determinadas características, utilizam a bordo um Automatic Identification System (AIS) que transmite sinal através de frequências rádio, permitindo que estações em terra recebam dados estáticos e dinâmicos do navio. Deste modo, verifica-se um aumento do conhecimento situacional marítimo e, consequentemente, da segurança da navegação. Foi realizada uma análise bibliométrica e de conteúdo a fim de avaliar o estado da arte referente a modelos de análise geoespacial do tráfego marítimo, com base em dados AIS, e foco na deteção de comportamentos anómalos. Na metodologia desta dissertação, são utilizados dados AIS mensais e diários na área de estudo, situada a sul de Portugal Continental. Foram criados mapas de densidade de tráfego marítimo com o apoio de um SIG (software QGIS), o que permitiu caracterizar o tráfego marítimo na área de estudo e, posteriormente, padronizar os locais onde ocorrem encontros entre navios. O algoritmo para detetar encontros entre navios no mar foi desenvolvido através de uma metodologia baseada em regras. Após análise e discussão de resultados, constatou-se que as áreas onde a possibilidade de ocorrer encontros de navios no mar é maior, encontram-se afastadas dos corredores principais de navegação, mas próximas de zonas com embarcações de pesca. Os resultados do estudo e o workflow desenvolvidos são úteis à tomada de decisão pelas autoridades competentes por patrulhar as áreas marítimas, com incidência na deteção de ações de transbordo ilegal

A facility to Search for Hidden Particles (SHiP) at the CERN SPS

A new general purpose fixed target facility is proposed at the CERN SPS accelerator which is aimed at exploring the domain of hidden particles and make measurements with tau neutrinos. Hidden particles are predicted by a large number of models beyond the Standard Model. The high intensity of the SPS 400~GeV beam allows probing a wide variety of models containing light long-lived exotic particles with masses below ${\cal O}$(10)~GeV/c$^2$, including very weakly interacting low-energy SUSY states. The experimental programme of the proposed facility is capable of being extended in the future, e.g. to include direct searches for Dark Matter and Lepton Flavour Violation.Comment: Technical Proposa

Enhancing AIS to Improve Whale-Ship Collision Avoidance and Maritime Security

Whale-ship strikes are of growing worldwide concern due to the steady growth of commercial shipping. Improving the current situation involves the creation of a communication capability allowing whale position information to be estimated and exchanged among vessels and other observation assets. An early example of such a system has been implemented for the shipping lane approaches to the harbor of Boston, Massachusetts where ship traffic transits areas of the Stellwagen Bank National Marine Sanctuary frequently used by whales. It uses the Automated Identification Systems (AIS) technology, currently required for larger vessels but becoming more common in all classes of vessels. However, we believe the default mode of AIS operation will be inadequate to meet the long-term needs of whale-ship collision avoidance, and will likewise fall short of meeting other current and future marine safety and security communication needs. This paper explores the emerging safety and security needs for vessel communications, and considers the consequences of a communication framework supporting asynchronous messaging that can be used to enhance the basic AIS capability. The options we analyze can be pursued within the AIS standardization process, or independently developed with attention to compatibility with existing AIS systems. Examples are discussed for minimizing ship interactions with Humpback Whales and endangered North Atlantic Right Whales on the east coast, and North Pacific Right Whales, Bowhead Whales, Humpback Whales, Blue Whales and Beluga Whales in west coast, Alaskan and Hawaiian waters

Ship and Oil-Spill Detection Using the Degree of Polarization in Linear and Hybrid/Compact Dual-Pol SAR

Monitoring and detection of ships and oil spills using synthetic aperture radar (SAR) have received a considerable attention over the past few years, notably due to the wide area coverage and day and night all-weather capabilities of SAR systems. Among different polarimetric SAR modes, dual-pol SAR data are widely used for monitoring large ocean and coastal areas. The degree of polarization (DoP) is a fundamental quantity characterizing a partially polarized electromagnetic field, with significantly less computational complexity, readily adaptable for on-board implementation, compared with other well-known polarimetric discriminators. The performance of the DoP is studied for joint ship and oil-spill detection under different polarizations in hybrid/compact and linear dual-pol SAR imagery. Experiments are performed on RADARSAT-2 -band polarimetric data sets, over San Francisco Bay, and -band NASA/JPL UAVSAR data, covering the Deepwater Horizon oil spill in the Gulf of Mexico