• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.2
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• pp.213-218
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• 2013

In this paper, a video based ship detection method is proposed to monitor port efficiently. Visual saliency map algorithm and mean shift algorithm is applied to detect moving ships don't include background information which is difficult to track moving ships. It is easy to detect ships at the port using saliency map algorithm, because it is very effective to extract saliency object from background. To remove background information in the saliency region, image segmentation and clustering using mean shift algorithm is used. As results of detecting simulation with images of a camera installed at the harbor, it is shown that the proposed method is effective to detect ships.

• Korean Journal of Remote Sensing
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• v.34 no.2_2
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• pp.313-326
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• 2018

This paper introduces core techniques on ship detection and tracking based on a compact HF radar platform which is necessary to establish a wide-area surveillance network. Currently, most HF radar sites are primarily optimized for observing sea surface radial velocities and bearings. Therefore, many ship detection systems are vulnerable to error sources such as environmental noise and clutter when they are applied to these practical surface current observation purpose systems. In addition, due to Korea's geographical features, only compact HF radars which generates non-uniform antenna response and has no information on target information are applicable. The ship detection and tracking techniques discussed in this paper considers these practical conditions and were evaluated by real data collected from the Yellow Sea, Korea. The proposed method is composed of two parts. In the first part, ship detection, a constant false alarm rate based detector was applied and was enhanced by a PCA subspace decomposition method which reduces noise. To merge multiple detections originated from a single target due to the Doppler effect during long CPIs, a clustering method was applied. Finally, data association framework eliminates false detections by considering ship maneuvering over time. According to evaluation results, it is claimed that the proposed method produces satisfactory results within certain ranges.

• Journal of Korea Multimedia Society
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• v.15 no.11
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• pp.1284-1291
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• 2012

Ship object detection is a technique to detect the existence and the location of ship when ship objects are shown on input image sequence, and there are wide variations in accuracy due to environmental changes and noise of input image. In order to solve this problem, in this paper, we propose multiple ship object detection based on background registration technique and morphology operation. The proposed method consists of the following five steps: background elimination step, noise elimination step, object standard position setting step, object restructure step, and multiple object detection steps. The experimental results show accurate and real-time ship detection for 15 different test sequences with a detection rate of 98.7%, and robustness against variable environment. The proposed method may be helpful as the base technique of sea surface monitoring or automatic ship sailing.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2023.05a
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• pp.212-213
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• 2023

항·포구내에서 주.야간에 걸쳐 미등록 선박을 검출하기 위해, 가시광 및 IR, 라이다 센서를 통해 선박 영상 및 거리정보를 획득하고, 딥러닝 기술을 적용하여 선박의 외관에 대한 특징 분석 및 선박에 표기된 문자열의 인식, 선박의 크기 측정을 통해 선박을 분류하고 특정하는 기술 개발

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.12
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• pp.2636-2641
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• 2010

To support anti-collision between ship to ship and sea-search and sea-rescue work, ship automatic identification system(AIS) that can both send and receive messages between ship and VTS Traffic control have been adopted. And port control system can control traffic vessel service which is co-operated with AIS. For more efficient traffic vessel service, ship recognition and display system is required to cooperated with AIS. In this paper, we propose ship detection system which is co-operated with AIS by using background estimation based on image processing for on the sea or harbor image extracted from camera. We experiment with on the sea or harbor image extracted from real-time input image from camera. By computer simulation and real world test, the proposed system show more effective to ship monitoring.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.10a
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• pp.271-273
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• 2010

In this paper, we propose ship detection system which is co-operated with AIS by using background estimation based on image processing for on the sea or harbor image extracted from camera. We experiment with on the sea or harbor image extracted from real-time input image from camera. By computer simulation and real world test, the proposed system show more effective to ship monitoring.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.5
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• pp.90-96
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• 2017

Due to the merit of having wide range with low cost, HF radar's ship detection and tracking research as maritime surveillance system has been recently studied. Many ship detection and tracking algorithms have been developed so far, however, performance comparison cannot be conducted properly because the states of target ships (such as moving path, size, etc.) differ from each study. In this paper, we propose a simulator based on compact HF radar, which generates data according to the size and moving path of target ship. Given the generated data with identical ship state, it is possible to conduct performance comparison. In order to validate the proposed simulator, the simulated data has been compared with real data collected by the SeaSonde HF radar sites. As a result, it has been shown that our simulated data resembles the real data. Therefore, the performance of various detection or tracking algorithms can be compared and analyzed respectively by using our simulated data.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.06a
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• pp.381-382
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• 2022

흘수는 선체가 물속에 얼마나 잠겨있는지를 나타내는 용어로, 선박에서는 화물의 양을 계산하거나 안정성을 평가하기 위해 흘수를 측정한다. 흘수를 측정하는 방법으로는 항해사가 부두에서 육안으로 확인하거나, 사다리를 타고 내려가 직접 확인하는 방법이 있다. 이러한 방법들은 경우에 따라 흘수 측정이 불가능하거나, 추락의 위험이 항상 존재한다는 문제가 있다. 이러한 문제를 해결하기 위해 드론 등을 통해 카메라로 선박의 흘수선 부근을 촬영하고, 필터링 및 이미지 검출 기법을 사용하여 선박의 흘수선을 탐지하는 방안을 제시하였다.

• Journal of the Korean Society of Marine Environment & Safety
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• v.26 no.6
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• pp.601-605
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• 2020

In large ships with complex structures, it is difficult to locate workers. In particular, it is not easy to detect when a worker falls down, making it difficult to respond quickly. Thus, research is being conducted to detect fallen workers using a camera or by attaching a device to the body. Existing image-based fall detection systems have been designed to detect a person's body parts; hence, it is difficult to detect them in various ships and postures. In this study, the entire fall area was extracted and deep learning was used to detect the fallen shipworker based on the image. The data necessary for learning were obtained by recording falling states at the shipyard. The amount of learning data was augmented by flipping, resizing, and rotating the image. Performance evaluation was conducted with precision, reproducibility, accuracy, and a low error rate. The larger the amount of data, the better the precision. In the future, reinforcing various data is expected to improve the effectiveness of camera-based fall detection models, and thus improve safety.

• Journal of the Korean Society of Marine Environment & Safety
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• v.25 no.4
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• pp.476-481
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• 2019

Early detection of fire is an important measure for minimizing the loss of life and property damage. However, fire and smoke need to be simultaneously detected. In this context, numerous studies have been conducted on image-based fire detection. Conventional fire detection methods are compute-intensive and comprise several algorithms for extracting the flame and smoke characteristics. Hence, deep learning algorithms and convolution neural networks can be alternatively employed for fire detection. In this study, recorded image data of fire in a ship engine room were analyzed. The flame and smoke characteristics were extracted from the outer box, and the YOLO (You Only Look Once) convolutional neural network algorithm was subsequently employed for learning and testing. Experimental results were evaluated with respect to three attributes, namely detection rate, error rate, and accuracy. The respective values of detection rate, error rate, and accuracy are found to be 0.994, 0.011, and 0.998 for the flame, 0.978, 0.021, and 0.978 for the smoke, and the calculation time is found to be 0.009 s.