Automatic Detection of Nearby Ships using Monocular Vision for Autonomous Navigation of USVs

Park, Jeonghong; Han, Jung Wook; Kim, Jinwhan; Son, Nam-Sun; Young, Kim Sun

doi:10.5302/j.icros.2017.17.0042

Cited by 8 publications

(4 citation statements)

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“…For instance, moving ships (as shown in Fig. 1 c), floating obstacles and other traffic participants can increase the risk and make a navigation scene more complex 31 . Camera shaking Vision-based navigation scenes have to consider the camera shaking phenomenon, which is caused by wind, waves and currents on the water.…”

Section: Vision-based Navigation Scenementioning

confidence: 99%

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Research on the visual image-based complexity perception method of autonomous navigation scenes for unmanned surface vehicles

Shi

Guo

Wang³

et al. 2022

Sci Rep

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To solve the long-tail problem and improve the testing efficiency for autonomous navigation systems of unmanned surface vehicles (USVs), a visual image-based navigation scene complexity perception method is proposed. In this paper, we intend to accurately construct a mathematical model between navigation scene complexity and visual features from the analysis and processing of image textures. First, the typical complex elements are summarized, and the navigation scenes are divided into four levels according to whether they contain these typical elements. Second, the textural features are extracted using the gray level cogeneration matrix (GLCM) and Tamura coarseness, which are applied to construct the feature vectors of the navigation scenes. Furthermore, a novel paired bare bone particle swarm clustering (PBBPSC) method is proposed to classify the levels of complexity, and the exact value of the navigation scene complexity is calculated using the clustering result and an interval mapping method. By comparing different methods on the classical and self-collected datasets, the experimental results show that our proposed complexity perception method can not only better describe the level of complexity of navigation scenes but also obtain more accurate complexity values.

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Section: Vision-based Navigation Scenementioning

confidence: 99%

“…For instance, moving ships (as shown in Fig. 1 c), floating obstacles and other traffic participants can increase the risk and make a navigation scene more complex 31 .…”

Section: Vision-based Navigation Scenementioning

confidence: 99%

Research on the visual image-based complexity perception method of autonomous navigation scenes for unmanned surface vehicles

Shi

Guo

Wang³

et al. 2022

Sci Rep

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“…However, the interference information can be eliminated, and the accuracy of obstacle detection can be improved after the sea-sky-line is detected. In the process of maritime autonomous navigation and path planning, [9][10][11][12] distant targets, such as ships and buoys, generally lie in the neighborhood of the sea-sky-line and break the linearity of the sea-sky-line. Locating the region of sea-sky-line and narrowing the search range, a target object in the distance can be recognized rapidly, and the precision can be enhanced.…”

Section: Introductionmentioning

confidence: 99%

A sea-sky-line detection method based on Gaussian mixture models and image texture features

Yang

Liu

et al. 2019

International Journal of Advanced Robotic Systems

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This article presents a sea-sky-line detection algorithm in a sea-sky environment for unmanned surface vehicles. Obstacle detection is a vital branch for unmanned surface vehicles on the ocean. Because of the specificity and complexity of the marine navigation environment, we first apply semantic segmentation for marine images. The complete marine scene is divided into sky area, middle mixture area, and seawater area before sea-sky-line detection. Segmenting the marine environment is beneficial for narrowing the obstacle search area, accelerating the rate of obstacle detection, and improving detection accuracy. Therefore, a fast, robust, and accurate sea-sky image segmentation method is urgently required. Therefore, we present a method that lies in a probabilistic graphical model for segmenting marine images. The Gaussian mixture model is introduced as the probability distribution model for the marine image. The sky, middle mixture, and seawater areas are generated by three Gaussian models. The expectation–maximization algorithm is utilized to maximize the log-likelihood function, and the parameters of the Gaussian mixture probability density function that recover the marine image distribution are available after several iterations. Furthermore, to solve the problem of incorrect convergence direction caused by unsatisfactory initialization conditions, the gray level co-occurrence matrix is referenced to initialize the Gaussian components. The coarse segmentation results rely on the gray level co-occurrence matrix and are used to calculate the prior initialization parameters of Gaussian components and obtain the prior distribution information of marine images, which mitigates the harmful influence of poor initialization. The algorithm is tested on a data set consisting of the marine obstacle detection dataset (MODD) public data set and our collected images. The results on this data set demonstrate that the proposed method is more robust and that a superior initialization condition can effectively accelerate the convergence velocity of the iterative process for Gaussian components.

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“…In order for the USV to be operated autonomously, an artificial intelligence system methodology that recognizes, judges, and plans the situation like a human being is needed. This means a complex process that can make decisions by self-determination of the state of operation and surrounding conditions of various sensors and armed devices [4] In addition, Modeling & Simulation (M&S) techniques, which are generally used in the defense field, are used for system design, development, analysis and acquisition, and especially for mission analysis [11] [12]. Therefore, in this study, we defined an Experimental Framework (EF) for analyzing the task of USV using the M&S technique and designed the interface.…”

Section: Introductionmentioning

confidence: 99%

Remote Operation SW for USV: Part II. Simulation Development

Heo¹,

Hwang²,

Kwon³

2018

WJET

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For contingencies occurring in a complex marine environment, Unmanned Surface Vehicle (USV) has to recognize the situation and decide behaviors, and plan the following actions through the Integrated Mission Planning Process. Therefore, researches are actively being carried out about it. However, since it is difficult to test the actual USV with the mission planning process, it is necessary to develop a virtual experimental environment based on Modeling & Simulation (M&S). In this study, we developed an integrated simulation environment capable of simulating and analyzing the overall mission of USV. In Part I, we modelled the USV Integrated Mission Planning Process and in Part II, we developed an experimental framework and interface for loading them. In addition, we verified the suitability of this model through scenarios and defined the Mission of Effectiveness (MOE) concept for USV mission analysis.

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Automatic Detection of Nearby Ships using Monocular Vision for Autonomous Navigation of USVs

Cited by 8 publications

References 0 publications

Research on the visual image-based complexity perception method of autonomous navigation scenes for unmanned surface vehicles

Research on the visual image-based complexity perception method of autonomous navigation scenes for unmanned surface vehicles

A sea-sky-line detection method based on Gaussian mixture models and image texture features

Remote Operation SW for USV: Part II. Simulation Development

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