8,498 research outputs found
A Dimensionality Reduction-Based Multi-Step Clustering Method for Robust Vessel Trajectory Analysis
The Shipboard Automatic Identification System (AIS) is crucial for navigation safety and maritime surveillance, data mining and pattern analysis of AIS information have attracted considerable attention in terms of both basic research and practical applications. Clustering of spatio-temporal AIS trajectories can be used to identify abnormal patterns and mine customary route data for transportation safety. Thus, the capacities of navigation safety and maritime traffic monitoring could be enhanced correspondingly. However, trajectory clustering is often sensitive to undesirable outliers and is essentially more complex compared with traditional point clustering. To overcome this limitation, a multi-step trajectory clustering method is proposed in this paper for robust AIS trajectory clustering. In particular, the Dynamic Time Warping (DTW), a similarity measurement method, is introduced in the first step to measure the distances between different trajectories. The calculated distances, inversely proportional to the similarities, constitute a distance matrix in the second step. Furthermore, as a widely-used dimensional reduction method, Principal Component Analysis (PCA) is exploited to decompose the obtained distance matrix. In particular, the top k principal components with above 95% accumulative contribution rate are extracted by PCA, and the number of the centers k is chosen. The k centers are found by the improved center automatically selection algorithm. In the last step, the improved center clustering algorithm with k clusters is implemented on the distance matrix to achieve the final AIS trajectory clustering results. In order to improve the accuracy of the proposed multi-step clustering algorithm, an automatic algorithm for choosing the k clusters is developed according to the similarity distance. Numerous experiments on realistic AIS trajectory datasets in the bridge area waterway and Mississippi River have been implemented to compare our proposed method with traditional spectral clustering and fast affinity propagation clustering. Experimental results have illustrated its superior performance in terms of quantitative and qualitative evaluation
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
Are object detection assessment criteria ready for maritime computer vision?
Maritime vessels equipped with visible and infrared cameras can complement
other conventional sensors for object detection. However, application of
computer vision techniques in maritime domain received attention only recently.
The maritime environment offers its own unique requirements and challenges.
Assessment of the quality of detections is a fundamental need in computer
vision. However, the conventional assessment metrics suitable for usual object
detection are deficient in the maritime setting. Thus, a large body of related
work in computer vision appears inapplicable to the maritime setting at the
first sight. We discuss the problem of defining assessment metrics suitable for
maritime computer vision. We consider new bottom edge proximity metrics as
assessment metrics for maritime computer vision. These metrics indicate that
existing computer vision approaches are indeed promising for maritime computer
vision and can play a foundational role in the emerging field of maritime
computer vision
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly
become a methodology of choice for analyzing medical images. This paper reviews
the major deep learning concepts pertinent to medical image analysis and
summarizes over 300 contributions to the field, most of which appeared in the
last year. We survey the use of deep learning for image classification, object
detection, segmentation, registration, and other tasks and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.Comment: Revised survey includes expanded discussion section and reworked
introductory section on common deep architectures. Added missed papers from
before Feb 1st 201
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Maritime data integration and analysis: Recent progress and research challenges
The correlated exploitation of heterogeneous data sources offering very large historical as well as streaming data is important to increasing the accuracy of computations when analysing and predicting future states of moving entities. This is particularly critical in the maritime domain, where online tracking, early recognition of events, and real-time forecast of anticipated trajectories of vessels are crucial to safety and operations at sea. The objective of this paper is to review current research challenges and trends tied to the integration, management, analysis, and visualization of objects moving at sea as well as a few suggestions for a successful development of maritime forecasting and decision-support systems
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