Sea surface ships detection method of UAV based on improved YOLOv3

2020 ◽  
Author(s):  
Zhang Xaingfu ◽  
Zhangsong Shi ◽  
Zhonghong Wu ◽  
Jian Liu
Keyword(s):  
Detection Method ◽  
Sea Surface

scholarly journals Defining Southern Ocean fronts using unsupervised classification

Ocean Science ◽  
2021 ◽  
Vol 17 (6) ◽  
pp. 1545-1562
Author(s):  
Simon D. A. Thomas ◽  
Daniel C. Jones ◽  
Anita Faul ◽  
Erik Mackie ◽  
Etienne Pauthenet
Keyword(s):  
Southern Ocean ◽  
Edge Detection ◽  
Detection Method ◽  
Unsupervised Classification ◽  
Sea Surface Height ◽  
Sea Surface ◽  
Near Surface ◽  
Ocean Fronts ◽  
Dynamic Height ◽  
Edge Detection Method

Abstract. Oceanographic fronts are transitions between thermohaline structures with different characteristics. Such transitions are ubiquitous, and their locations and properties affect how the ocean operates as part of the global climate system. In the Southern Ocean, fronts have classically been defined using a small number of continuous, circumpolar features in sea surface height or dynamic height. Modern observational and theoretical developments are challenging and expanding this traditional framework to accommodate a more complex view of fronts. Here, we present a complementary new approach for calculating fronts using an unsupervised classification method called Gaussian mixture modelling (GMM) and a novel inter-class parameter called the I-metric. The I-metric approach produces a probabilistic view of front location, emphasising the fact that the boundaries between water masses are not uniformly sharp across the entire Southern Ocean. The I-metric approach uses thermohaline information from a range of depth levels, making it more general than approaches that only use near-surface properties. We train the GMM using an observationally constrained state estimate in order to have more uniform spatial and temporal data coverage. The probabilistic boundaries defined by the I-metric roughly coincide with several classically defined fronts, offering a novel view of this structure. The I-metric fronts appear to be relatively sharp in the open ocean and somewhat diffuse near large topographic features, possibly highlighting the importance of topographically induced mixing. For comparison with a more localised method, we also use an edge detection approach for identifying fronts. We find a strong correlation between the edge field of the leading principal component and the zonal velocity; the edge detection method highlights the presence of jets, which are supported by thermal wind balance. This more localised method highlights the complex, multiscale structure of Southern Ocean fronts, complementing and contrasting with the more domain-wide view offered by the I-metric. The Sobel edge detection method may be useful for defining and tracking smaller-scale fronts and jets in model or reanalysis data. The I-metric approach may prove to be a useful method for inter-model comparison, as it uses the thermohaline structure of those models instead of tracking somewhat ad hoc values of sea surface height and/or dynamic height, which can vary considerably between models. In addition, the general I-metric approach allows front definitions to shift with changing temperature and salinity structures, which may be useful for characterising fronts in a changing climate.

scholarly journals Improved YOLOv4 Marine Target Detection Combined with CBAM

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 623
Author(s):  
Huixuan Fu ◽  
Guoqing Song ◽  
Yuchao Wang
Keyword(s):  
Target Detection ◽  
Detection Method ◽  
Feature Learning ◽  
Sea Surface ◽  
Detection Accuracy ◽  
Detection Algorithms ◽  
Detection Technology ◽  
Time Requirements ◽  
Small Targets ◽  
Sea Area

Marine target detection technology plays an important role in sea surface monitoring, sea area management, ship collision avoidance, and other fields. Traditional marine target detection algorithms cannot meet the requirements of accuracy and speed. This article uses the advantages of deep learning in big data feature learning to propose the YOLOv4 marine target detection method fused with a convolutional attention module. Marine target detection datasets were collected and produced and marine targets were divided into ten categories, including speedboat, warship, passenger ship, cargo ship, sailboat, tugboat, and kayak. Aiming at the problem of insufficient detection accuracy of YOLOv4’s self-built marine target dataset, a convolutional attention module is added to the YOLOv4 network to increase the weight of useful features while suppressing the weight of invalid features to improve detection accuracy. The experimental results show that the improved YOLOv4 has higher detection accuracy than the original YOLOv4, and has better detection results for small targets, multiple targets, and overlapping targets. The detection speed meets the real-time requirements, verifying the effectiveness of the improved algorithm.

scholarly journals Small and Dim Ship Target Detection Based on Sea-Sky-Line

2019 ◽  
Vol 37 (1) ◽  
pp. 35-40 ◽  
Author(s):  
Yaohui Hu ◽  
Ke Zhang ◽  
Chao Xing
Keyword(s):  
Target Detection ◽  
Detection Method ◽  
Detection Algorithm ◽  
Sea Surface ◽  
Line Detection ◽  
Convolutional Networks ◽  
Fully Convolutional Networks ◽  
Noise Point ◽  
The Mean ◽  
Potential Area

In order to solve the problem of small and dim ship target detection under complex sea-sky background, we propose a target detection algorithm based on sea-sky line detection. Firstly, the paper locates the sea-sky-line based on fully convolutional networks, through which target potential area can be determined and disturbance can be excluded. Then the method based on the mean of four detection gradient is adopted to detect the small and dim ship target. The simulation results show that the method of sea-sky-line detection based on fully convolutional networks can overcome the disadvantages of the traditional methods and is suitable for complex background. The detection method proposed can filter the white noise point on the sea surface and thus can reduce false alarm, through which the detection of small and dim ship can be completed well.

Solid-phase immunoelectron microscopy for rapid diagnosis of enteroviruses

1984 ◽  
Vol 42 ◽  
pp. 226-227 ◽  
Author(s):  
K. Pegg-Feige ◽  
F. W. Doane
Keyword(s):  
Electron Microscopy ◽  
Cell Culture ◽  
Immunoelectron Microscopy ◽  
Detection Method ◽  
Solid Phase ◽  
Protein A ◽  
Plant Viruses ◽  
Rapid Diagnosis ◽  
Virus Diagnosis ◽  
Antiviral Antibody

Immunoelectron microscopy (IEM) applied to rapid virus diagnosis offers a more sensitive detection method than direct electron microscopy (DEM), and can also be used to serotype viruses. One of several IEM techniques is that introduced by Derrick in 1972, in which antiviral antibody is attached to the support film of an EM specimen grid. Originally developed for plant viruses, it has recently been applied to several animal viruses, especially rotaviruses. We have investigated the use of this solid phase IEM technique (SPIEM) in detecting and identifying enteroviruses (in the form of crude cell culture isolates), and have compared it with a modified “SPIEM-SPA” method in which grids are coated with protein A from Staphylococcus aureus prior to exposure to antiserum.

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