• Smart Structures and Systems
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• 제23권4호
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• pp.359-371
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• 2019

As part of a structural health monitoring system, the relative geometric relationship between a ship and bridge has been recognized as important for bridge authorities and ship owners to avoid ship-bridge collision. This study proposes a novel computer vision method for the real-time geometric parameter identification of moving ships based on a single shot multibox detector (SSD) by using transfer learning techniques and monocular vision. The identification framework consists of ship detection (coarse scale) and geometric parameter calculation (fine scale) modules. For the ship detection, the SSD, which is a deep learning algorithm, was employed and fine-tuned by ship image samples downloaded from the Internet to obtain the rectangle regions of interest in the coarse scale. Subsequently, for the geometric parameter calculation, an accurate ship contour is created using morphological operations within the saturation channel in hue, saturation, and value color space. Furthermore, a local coordinate system was constructed using projective geometry transformation to calculate the geometric parameters of ships, such as width, length, height, localization, and velocity. The application of the proposed method to in situ video images, obtained from cameras set on the girder of the Wuhan Yangtze River Bridge above the shipping channel, confirmed the efficiency, accuracy, and effectiveness of the proposed method.

• International journal of advanced smart convergence
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• 제4권2호
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• pp.46-53
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• 2015

This paper suggests a method of real time confidence interval estimation to detect abnormal states of sensor data. For real time confidence interval estimation, the mean square errors of the exponential smoothing method and moving average method, two of the time series analysis method, were compared, and the moving average method with less errors was applied. When the sensor data passes the bounds of the confidence interval estimation, the administrator is notified through alarms. As the suggested method is for real time anomaly detection in a ship, an Android terminal was adopted for better communication between the wireless sensor network and users. For safe navigation, an administrator can make decisions promptly and accurately upon emergency situation in a ship by referring to the anomaly detection information through real time confidence interval estimation.

• Journal of information and communication convergence engineering
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• 제12권3호
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• pp.168-172
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• 2014

In this paper, we propose an algorithm that can efficiently detect and monitor multiple ships in real-time. The proposed algorithm uses morphological operations and edge information for detecting and tracking ships. We used smoothing filter with a $3{\times}3$ Gaussian window and luminance component instead of RGB components in the captured image. Additionally, we applied Sobel operator for edge detection and a threshold for binary images. Finally, object labeling with connectivity and morphological operation with open and erosion were used for ship detection. Compared with conventional methods, the proposed method is meant to be used mainly in coastal surveillance systems and monitoring systems of harbors. A system based on this method was tested for both stationary and non-stationary backgrounds, and the results of the detection and tracking rates were more than 97% on average. Thousands of image frames and 20 different video sequences in both online and offline modes were tested, and an overall detection rate of 97.6% was achieved.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2000년도 추계학술대회 논문집
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• pp.695-698
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• 2000

Round-cornering machines are mainly used for cornering of stiffners for ship buildings. In the present time they have been operated manually by operators. so they are need to be operated automatically without regard to any shapes of stiffners. We developed the automatic round cornering system which consists of CCd Camera, PC and laser diode to detect automatically the edge of stiffners to be processed

• Ocean Systems Engineering
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• 제10권4호
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• pp.415-434
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• 2020

Detecting objects is important for the safe operation of ships, and enables collision avoidance, risk detection, and autonomous sailing. This study proposes a ship detection method from images and videos taken at sea using one of the state-of-the-art deep neural network-based object detection algorithms. A deep learning model is trained using a public maritime dataset, and results show it can detect all types of floating objects and classify them into ten specific classes that include a ship, speedboat, and buoy. The proposed deep learning model is compared to a universal trained model that detects and classifies objects into general classes, such as a person, dog, car, and boat, and results show that the proposed model outperforms the other in the detection of maritime objects. Different deep neural network structures are then compared to obtain the best detection performance. The proposed model also shows a real-time detection speed of approximately 30 frames per second. Hence, it is expected that the proposed model can be used to detect maritime objects and reduce risks while at sea.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2010년도 추계학술대회
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• pp.271-273
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• 2010

본 논문에서는 카메라로부터 입력된 해상 또는 항만 영상에 대하여 배경추정을 이용한 영상기반의 선박검출과 해당 선박의 AIS 신호를 연동하여 모니터 상에 표출하는 AIS 연동 선박검출시스템을 제안한다. 해상 또는 항만에서 실시간 입력되는 영상에 대하여 선박 검출 실험을 하였다. 시뮬레이션 및 실 환경에서의 실험결과 제안하는 알고리즘이 선박 관제에 효과적인 것을 확인하였다.

• 한국멀티미디어학회논문지
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• 제15권11호
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• pp.1284-1291
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• 2012

선박 객체 검출 기술은 입력된 비디오 및 영상 데이터에서 선박 객체가 존재하는 경우 선박의 위치를 검출하는 기술로서 입력 영상의 환경 변화와 잡음의 영향에 따라서 검출 정확도의 편차가 높다. 이런 문제점을 해결하기 위하여 본 논문에서는 배경 구축 기법과 형태학적 연산 기반의 다중 선박 객체 검출 기술을 제안한다. 제안하는 방법은 배경 제거 단계, 잡음 제거 단계, 객체 기준 위치 설정 단계, 객체 재구성 단계, 다중 객체 검출 단계 등 5단계를 거쳐서 선박을 검출한다. 다양한 변수를 고려한 15가지 실험 비디오를 대상으로 한 실험을 통해서 98.7%의 검출율을 나타내었으며, 환경 변화에 강인한 검출을 수행하는 것을 확인할 수 있었다. 제안하는 방법은 해상 관제와 선박 자동 운항 기술의 기반 기술로서 유용하게 사용될 수 있다.

• 대한원격탐사학회지
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• 제37권3호
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• pp.559-566
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• 2021

최근 증가된 해상 교통량의 영향으로 지속적으로 발생하는 선박사고에 대한 신속한 탐지 및 대처가 필요하다. 이를 위해, 광역 범위로 실시간 모니터링이 가능한 위성영상을 기반으로 선박탐지 연구가 활발히 수행되고 있다. 그러나, 분광특성을 활용하여 선박탐지를 수행한 선행연구에서는 후류(Wake) 제거를 수행하지 않아 후류가 선박으로 오탐지될 가능성이 존재한다. 이에 본 연구에서는 Ship Detection Index (SDI)를 이용하여 Sentinel-2A/Multispectral Instrument (MSI) 위성영상에서 선박탐지를 수행하고 선박과 후류의 분광특성 차이를 기반으로 하는 Wake Detection Index (WDI)를 활용하여 후류를 제거하였다. 본 연구의 선박탐지 알고리즘의 정확도 검증을 위해 Probability of detection (POD), False alarm rate (FAR) 지수를 활용하였으며, 검증 결과 SDI만 적용한 결과에 비해 POD는 유사하게 나타나고 FAR는 6.4% 개선되었다.

• 한국정보통신학회논문지
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• 제14권12호
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• pp.2636-2641
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• 2010

선박과 선박, 선박과 육상 관제소간에 선박의 위치정보 등을 자동 송수신하여 선박 간의 충돌 방지 및 해난 수색 구조 활동을 지원하기 위하여 선박 자동 식별 장치인 AIS(automatic identification system)을 채용하고 있다. 그리고, 항만의 관제시스템은 선박 AIS와 연계하여 선박의 통항을 관리한다. 효율적인 통항관리를 할 수 있도록 AIS 연동하는 선박 인식 및 표출 시스템이 요구되고 있다. 본 논문에서는 카메라로부터 입력된 해상 또는 항만 영상에 대하여 배경추정을 이용한 영상기반의 선박검출과 검출된 해당 선박의 AIS 신호를 연동하여 모니터 상에 표출하는 AIS 연동 선박검출 방법을 제안한다. 해상 또는 항만에서 실시간 입력되는 영상에 대하여 선박 검출 실험을 하였다. 시뮬레이션 및 실험결과 제안하는 알고리즘이 항만의 선박 관제에 효과적으로 활용할 수 있음을 확인하였다.

• 대한임베디드공학회논문지
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• 제17권5호
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• pp.281-288
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• 2022

According to statistics conducted by the Korea Coast Guard, the number of accidents on small boats under 5 tons is increasing every year. This is because only a small number of people are on board. The previously developed maritime distress and safety systems are not well distributed because passengers must be equipped with additional remote equipment. The purpose of this study is to develop a distress awareness system that recognizes man over-board situations in real time. This study aims to present the part of the passenger tracking system among the small ship's distress awareness situational system that can generate passenger's location information in real time using deep learning based object detection and tracking technologies. The system consisted of the following steps. 1) the passenger location information is generated in the form of Bounding box using its detection model (YOLOv3). 2) Based on the Bounding box data, Deep SORT predicts the Bounding box's position in the next frame of the image with Kalman filter. 3) When the actual Bounding Box is created within the range predicted by Kalman-filter, Deep SORT repeats the process of recognizing it as the same object. 4) If the Bounding box deviates the ship's area or an error occurs in the number of tracking occupant, the system is decided the distress situation and issues an alert. This study is expected to complement the problems of existing technologies and ensure the safety of individuals aboard small boats.