• 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.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.93-94
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• 2022

최근 고령화 사회가 심각한 사회적 문제로 급부상하고 있으며, 이에 병원을 찾아 입원하는 비중이 이전에 비하여 높아지고 있다. 거동이 불편하거나 근력이 부족한 환자의 경우 스스로 거동할 능력이 다소 떨어지며, 낙상사고가 발생하면 부상 혹은 치명적일 경우 사망으로 이어질 수 있다. 하지만, 이들을 보살피는 간호 인력만으로 병원 내 모든 낙상사고를 파악하기에는 한계가 있다. 또한, 환자들의 낙상 탐지에 관한 연구는 지속해서 수행되어왔지만, 병원 내부환경에서의 낙상 탐지 연구는 부족하다. 이에 본 논문에서는 병원 내부환경에서 낙상을 탐지하기 위해 실제 병실에서 수집한 데이터로 YOLOv5 모델을 학습하여 환자 낙상 탐지모델을 구축 및 평가하였다.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.1
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• pp.153-158
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• 2016

A detection algorithm of fallen pear bags caused by natural disaster like heavy rain, typhoon, hurricane, etc. is presented in this paper. The algorithm is developed for the gray pear bags with printed characters which are widely used at pear farms at Sangju and Naju producing large quantity of pears for export. It sets a region of interest (ROI) at first and then eliminates the regions having chromatic color in ROI. Morphological operation and prior information are used to eliminate small noises and several unusual regions and finally the regions of fallen pear bags are remained. The remained regions are analyzed and counted to estimate the scale of damage. Test images are consisted of the images taken at pear farms of Sangju and Naju at 2014. Experimental result shows that the detection rate of pear bags is more than 90% and also the proposed system can be implemented in real-time using hand-held devices because of its simple and parallel architecture.

• Journal of The Korean Society For Urban Railway
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• v.6 no.4
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• pp.269-278
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• 2018

Railway obstacle detecion system has been introduced with high-speed railway in 2004 to prevent accidents by obstacles such as landslide, rockfall and things fallen from the gauntry over the railway. But existing system has some limitation for landslide or fallen obstacle over railway. Therefore, In this study, we suggest new advanced obstacle detection system introducing the OTDR, optical fiber fences and detection cameras. This system can detect depression degree by the force to the fences and video for the specific region as well as detection wire Off condition. We produce and functional tests for fiber fence and OTDR, which are the core parts of the development system, and results were obtained to demonstrate improved detection capabilities. Several functions also been tested to verify the advanced detection performance and got some satisfactory results. Further we will conduct environment tests and field test.

• Journal of information and communication convergence engineering
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• v.11 no.3
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• pp.199-206
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• 2013

An emergency monitoring system for the elderly, which uses acceleration data measured with an accelerometer, angular velocity data measured with a gyroscope, and heart rate measured with an electrocardiogram, is proposed. The proposed fall detection algorithm uses multiple parameter combinations in which all parameters, calculated using tri-axial accelerations and bi-axial angular velocities, are above a certain threshold within a time period. Further, we propose an emergency detection algorithm that monitors the movements of the fallen elderly person, after a fall is detected. The results show that the proposed algorithms can distinguish various types of falls from activities of daily living with 100% sensitivity and 98.75% specificity. In addition, when falls are detected, the emergency detection rate is 100%. This suggests that the presented fall and emergency detection method provides an effective automatic fall detection and emergency alarm system. The proposed algorithms are simple enough to be implemented into an embedded system such as 8051-based microcontroller with 128 kbyte ROM.

• Proceedings of the KSR Conference
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• 2007.05a
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• pp.953-958
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• 2007

Passenger safety is a primary concern of railway system but, it has been urgent issue that dozens of people are killed every year when they are fallen from train platforms. In this paper, we propose a vision based monitoring system for railway station platform. The system immediately perceives dangerous factors of passengers on the platform by using image processing technology. To monitor almost entire length of the track line in the platform, we use several video cameras. Each camera conducts surveillance its own preset monitoring area whether human or dangerous object was fallen in the area. Moreover, to deal with the accident immediately, the system provides local station, central control room employees and train driver with the video information about the accident situation including alarm message. This paper introduces the system overview and detection process with experimental results. According to the results, we expect the proposed system will play a key role for establishing highly intelligent monitoring system in railway.

• Journal of information and communication convergence engineering
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• v.9 no.3
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• pp.347-352
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• 2011

In this paper, although Radar has been used for a long time, integrated scheme with visual camera is an efficient way to enhance marine surveillance system. Camera image is focused by radar information but it is easy to be fallen into inaccurate operation due to environmental noises. We have proposed a method to filter the noises by moving average filter and Kalman filter. It is proved that Kalman filtered results preserves linearity while the former includes larger variance.

• Journal of the Optical Society of Korea
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• v.14 no.4
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• pp.451-457
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• 2010

Increasing demands on the safety of public train services have led to the development of various types of security monitoring systems. Most of the surveillance systems are focused on the estimation of crowd level in the platform, thereby yielding too many false alarms. In this paper, we present a novel security monitoring system to detect critically dangerous situations such as when a passenger falls from the station platform, or when a passenger walks on the rail tracks. The method is composed of two stages of detecting dangerous situations. Objects falling over to the dangerous zone are detected by motion tracking. 3D depth information retrieved by the stereo vision is used to confirm fallen events. Experimental results show that virtually no error of either false positive or false negative is found while providing highly reliable detection performance. Since stereo matching is performed on a local image only when potentially dangerous situations are found; real-time operation is feasible without using dedicated hardware.

• Proceedings of the KSR Conference
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• 2009.05a
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• pp.872-878
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• 2009

Passenger safety is a primary concern of the railway system. However, dozens of people are killed every year when they accidently fall on to the track from the boarding platform. This is one of the most urgent issues to solve regarding the railway platform. The installation environment, and the blind area problems as well as maintenance and operating costs which are not efficient have to be looked at. To solve these problems, we propose a 3D laser radar sensor based monitoring system for the railway platform. This paper introduces an overview, the detecting method, and the interface with the signalling system in detecting a fallen passenger from the platform using the 3D laser radar sensor.

• Proceedings of the KSR Conference
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• 2005.11a
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• pp.1039-1044
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• 2005

The Objective of advanced EMU Project is development of new technologies, that can resolve the system problems of conventional EMU, for reducing vehicle maintenance, improving passenger's service using IT and providing environmental friendly. As a concept of advanced EMU, We will develop the DDM with individual driving wheel for reducing of maintenance cost and full-electric braking system without pneumatic braking, and a new bogie for loadable DDM, and pre-diagnosis system which informs possible system error, and decentralization of vehicle control. To improve transport reliability, and energy storage system for saving energy, and fallen passenger detection system for improving passenger's safety at the platform.