An Overview on Horizon Detection Methods in Maritime Video Surveillance

Petković, Miro; Vujović, Igor; Kuzmanić, Ivica

doi:10.7225/toms.v09.n01.010

Cited by 9 publications

(6 citation statements)

References 21 publications

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“…Marine horizon detection is the most significant semantic boundary for segmenting the image into sea and sky. References [18,19] have summarized the marine horizon line detection. In the past research, many robust marine horizon generation methods have been proposed.…”

Section: Horizon Detectionmentioning

confidence: 99%

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Survey on Deep Learning-Based Marine Object Detection

Zhang

et al. 2021

Journal of Advanced Transportation

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We present a survey on marine object detection based on deep neural network approaches, which are state-of-the-art approaches for the development of autonomous ship navigation, maritime surveillance, shipping management, and other intelligent transportation system applications in the future. The fundamental task of maritime transportation surveillance and autonomous ship navigation is to construct a reachable visual perception system that requires high efficiency and high accuracy of marine object detection. Therefore, high-performance deep learning-based algorithms and high-quality marine-related datasets need to be summarized. This survey focuses on summarizing the methods and application scenarios of maritime object detection, analyzes the characteristics of different marine-related datasets, highlights the marine detection application of the YOLO series model, and also discusses the current limitations of object detection based on deep learning and possible breakthrough directions. The large-scale, multiscenario industrialized neural network training is an indispensable link to solve the practical application of marine object detection. A widely accepted and standardized large-scale marine object verification dataset should be proposed.

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Section: Horizon Detectionmentioning

confidence: 99%

“…Also, projection-based, region-based, hybrid, and artificial neural network (ANN) based methods for sea horizon detection have been discussed [19]. e researches of ANN methods in maritime surveillance made the horizon line detection easy, accurate, and robust.…”

Section: Introductionmentioning

confidence: 99%

Survey on Deep Learning-Based Marine Object Detection

Zhang

et al. 2021

Journal of Advanced Transportation

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“…This division helps isolate the area under observation which eventually decreases the computational cost associated with any further image processing task. For this reason, detection of the horizon line is considered a critical preprocessing step for maritime computer applications related to the navigation of unmanned aerial vehicles [1,2] and surface ships [3,4], collision avoidance [5,6], object detection [7] and tracking [8], surveillance [9], distance estimation [10], and video stabilization [11,12].…”

Section: Introductionmentioning

confidence: 99%

A Novel Visual-Range Sea Image Dataset for Sea Horizon Line Detection in Changing Maritime Scenes

Hashmani

Umair

2022

JMSE

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Sea horizon line (SHL) detection plays a pivotal role in the computational performance improvement of computer applications for the maritime environment by dividing the image into sea and sky regions. This division isolates the region of interest and reduces the computational cost of further processing. Testing and performance evaluation of SHL detection methods require a robust image dataset covering the maritime environment’s features at different geographical, seasonal, and maritime conditions. However, publicly available maritime image datasets are developed under a limited environment with slight-to-moderate variations in maritime features. This article proposes a novel sea image dataset that fills this gap by incorporating various geographical, seasonal, and maritime features. Across West Malaysia, one offshore and four geographically separated onshore locations were selected. On ten different occasions, field observations were recorded using a visual-range optical sensor and weather station. The data collection experiments were conducted between February 2020 until April 2021. The collected data were preprocessed and SHL images were selected based on their high feature diversity. Manual SHL annotation was applied on images, and a ground truth matrix was generated, which serves as a performance benchmark for SHL detection methods. As a result, the dataset presents 2673 high-definition (1920 × 1080 pixels) RGB images having a combination of 36 different geographical, seasonal, and maritime features to test and evaluate computer vision-based SHL detection methods.

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“…Our initial conceptual idea and initial proposal offer adding multiple (stereoscopic) visual channel of information as one more very (if not the most) informative source of control of movement parameters that can improve the stability and reliability of the movement vehicles control system and make it much more veracious. But this obvious at a first glance solution could not be implemented earlier [1][2][3][4][5][6][7][8] , since it requires continuous realization of large volume of very productive on-board computations in real-time mode and with quite small energy consumption. The lack of the required computational on-board performance can be solved with the help of the wireless access of aerial vehicle to the remote computational system with required performance [1] , but this approach is not quite reliable in comparison with any on-board solution.…”

Section: Introductionmentioning

confidence: 99%

“…Technologically NVIDIA supports the importance of sensors redundancy-the latest modification of the simplest and smallest realization of NVIDIA Jetson platform-Jetson Nano B01, issued in early 2020, was equipped with two MIPI CSI slots for in-built cameras. The most of existing schemes of visual support of navigation are based on a single-channel monocular vision [1,2,[4][5][6][7][8] , stereo vision approach, proposed and studied in [3], is dedicated for UAV detection as a target and in this case two video channels are needed for getting the distance to UAV, so, like in [1,2], this is not an on-board solution.…”

Section: Introductionmentioning

confidence: 99%

A New Concept of GPU-Accelerated On-Board Visual System for Aerial Vehicles

Shkvar

Dai

Shiju

et al. 2020

EST

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As technology advances, the level of intellectual ability and autonomy of the electronic-mechanical control system of modern aircrafts and spacecrafts is constantly growing, which helps to reduce the crew load and accident rate. But these helpful controlling systems are not perfect, under some unfavorable circumstances they get stuck or start to function unpredictably when faced with a much more complicated real situation than the developers expected, sometimes even lead to crashes. The concept of visual multichannel processing support of aircraft/spacecraft launch and landing as an additional element of automatic control loop for flight safety and reliability improvement is proposed and its advantages, feasibility and expediency are discussed and evaluated. The visual analyzers are very typical for the overwhelming majority of highly organized organisms (humans, animals, insects) as the most informative source of control of movement parameters, so they potentially can effectively improve the reliability of the entire embedded vehicle controlling system and, at the same time, their principal structure, implementation and further functioning are very similar and universal for real flight operation of different vehicles, which opens up great prospects for their application in engineering based on modern revolutionary achievements in the field of methodology and computational technologies for pattern recognition. In particular, it was shown that the required for real vehicles accuracy and productivity can be reached in case of developing the visual multichannel system, as an additional source of flight state information, on the base of NVIDIA Jetson embedded portable low power consuming Graphics processing unit (GPU)-accelerated massively parallel computational platform, providing CUDA and Artificial Intelligence data processing in real-time mode.

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An Overview on Horizon Detection Methods in Maritime Video Surveillance

Cited by 9 publications

References 21 publications

Survey on Deep Learning-Based Marine Object Detection

Survey on Deep Learning-Based Marine Object Detection

A Novel Visual-Range Sea Image Dataset for Sea Horizon Line Detection in Changing Maritime Scenes

A New Concept of GPU-Accelerated On-Board Visual System for Aerial Vehicles

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