• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.7 no.5
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• pp.64-76
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• 2008

To increase road safety at blackspots, it is needed to develop a new method that can process before accident occurrence. Accident situation could result from traffic conflict. Traffic conflict decision technique has an advantage that can acquire and analyze data in time and confined space that is less through investigation. Therefore, traffic conflict technique is highly expected to be used in many application of road safety. This study developed traffic conflict decision program that can analyze and process from signalized intersection image. Program consists of the following functional modules: an image input module that acquires images from the CCTV camera, a Save-to-Buffer module which stores the entered images by differentiating them into background images, current images, difference images, segmentation images, and a conflict detection module which displays the processed results. The program was developed using LabVIEW 8.5 (a graphic language) and the VISION module library.

• Journal of Korean Tunnelling and Underground Space Association
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• v.20 no.6
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• pp.1161-1175
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• 2018

An unexpected event could be easily followed by a large secondary accident due to the limitation in sight of drivers in road tunnels. Therefore, a series of automated incident detection systems have been under operation, which, however, appear in very low detection rates due to very low image qualities on CCTVs in tunnels. In order to overcome that limit, deep learning based tunnel incident detection system was developed, which already showed high detection rates in November of 2017. However, since the object detection process could deal with only still images, moving direction and speed of moving vehicles could not be identified. Furthermore it was hard to detect stopping and reverse the status of moving vehicles. Therefore, apart from the object detection, an object tracking method has been introduced and combined with the detection algorithm to track the moving vehicles. Also, stopping-reverse discrimination algorithm was proposed, thereby implementing into the combined incident detection processes. Each performance on detection of stopping, reverse driving and fire incident state were evaluated with showing 100% detection rate. But the detection for 'person' object appears relatively low success rate to 78.5%. Nevertheless, it is believed that the enlarged richness of image big-data could dramatically enhance the detection capacity of the automatic incident detection system.

• Nuclear Engineering and Technology
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• v.56 no.6
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• pp.2113-2119
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• 2024

This study presents the development and characterization of a prototype TL/OSL reader for the retrospective dose assessment of individuals in radiological emergencies. The reader is portable, semi-automatic, and capable of accurate measurements. The dimension of the reader is 25 × 25 × 37 cm³ and the weight is about 15 kg. The reader consists of a sample moving stage, a heating module, an optical stimulation module, a detection module, a data acquisition (DAQ) unit, a nitrogen gas control module, and a PC with a GUI program. The reader has three measurement modes: TL, CW_OSL, and custom mode. The reader was characterized using commercial thermal luminescence dosimeters (TLD, LiF:Mg,Cu,Si) and optically stimulated dosimeters (OSLD, Al₂O₃:C), as well as fortuitous materials, such as display glasses and resistors of mobile phone. The results showed that the reader is capable of measuring signals with a detection limit of up to 0.02 mGy using a commercial dosimeter. In the dose recovery test using fortuitous materials, the reconstructed doses obtained three days post-irradiation closely aligned with the initially administered doses. As a result, this study suggests that the developed TL/OSL reader is a promising instrument for emergency dose assessment at accident sites.

• Journal of the Korean Society of Safety
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• v.34 no.6
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• pp.29-37
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• 2019

Since construction sites are exposed to outdoor environments, working conditions are significantly dangerous. Thus, wearing of the personal protective equipments such as safety helmet is very important for worker safety. However, construction workers are often wearing-off the helmet as inconvenient and uncomportable. As a result, a small mistake may lead to serious accident. For this, checking of wearing safety helmet is important task to safety managers in field. However, due to the limited time and manpower, the checking can not be executed for every individual worker spread over a large construction site. Therefore, if an automatic checking system is provided, field safety management should be performed more effectively and efficiently. In this study, applicability of deep learning based computer vision technology is investigated for automatic checking of wearing safety helmet in construction sites. Faster R-CNN deep learning algorithm for object detection and classification is employed to develop the automatic checking model. Digital camera images captured in real construction site are used to validate the proposed model. Based on the results, it is concluded that the proposed model may effectively be used for automatic checking of wearing safety helmet in construction site.

• Journal of Korean Tunnelling and Underground Space Association
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• v.19 no.5
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• pp.813-827
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• 2017

Surveillance cameras installed in tunnels capture the various video frames effected by dynamic and variable factors. In addition, localizing and managing the cameras in tunnel is not affordable, and quality of capturing frame is effected by time. In this paper, we introduce a new method to detect and track the vehicles in tunnel by using surveillance cameras installed in a tunnel. It is difficult to detect the video frames directly from surveillance cameras due to the motion blur effect and blurring effect on lens by dirt. In order to overcome this difficulties, two new methods such as Differential Frame/Non-Maxima Suppression (DFNMS) and Haar Cascade Detector to track cars are proposed and investigated for their feasibilities. In the study, it was shown that high precision and recall values could be achieved by the two methods, which then be capable of providing practical data and key information to an automatic accident detection system in tunnels.

• Smart Media Journal
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• v.10 no.3
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• pp.31-38
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• 2021

It is hard to predict when and where a fall accident will happen. Also, if rapid follow-up measures on it are not performed, a fall accident leads to a threat of life, so studies that can automatically detect a fall accident have become necessary. Among automatic fall-accident detection techniques, a fall detection scheme using an IMU (inertial measurement unit) sensor attached to a wrist is difficult to detect a fall accident due to its movement, but it is recognized as a technique that is easy to wear and has excellent accessibility. To overcome the difficulty in obtaining fall data, this study proposes an algorithm that efficiently learns less data through machine learning such as KNN (k-nearest neighbors) and SVM (support vector machine). In addition, to improve the performance of these mathematical classifiers, this study utilized feature data aquired in the frequency space. The proposed algorithm analyzed the effect by diversifying the parameters of the model and the parameters of the frequency feature extractor through experiments using standard datasets. The proposed algorithm could adequately cope with a realistic problem that fall data are difficult to obtain. Because it is lighter than other classifiers, this algorithm was also easy to implement in small embedded systems where SIMD (single instruction multiple data) processing devices were difficult to mount.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.253-256
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• 2005

The use of satellite remote sensing in maritime safety and security can aid in the detection of illegal fishing activities and provide more efficient use of limited aircraft or patrol craft resources. In the area of vessel traffic monitoring for commercial vessels, Vessel Traffic Service (VTS) which use the ground-based radar system have some difficulties in detecting moving ships due to the limited detection range. A virtual vessel traffic control system is introduced to contribute to prevent a marine accident such as collision and stranding from happening. Existing VTS has its limit. The virtual vessel traffic control system consists of both data acquisition by satellite remote sensing and a simulation of traffic environment stress based on the satellite data, remotely sensed data. And it could be used to provide timely and detailed information about the marine safety, including the location, speed and direction of ships, and help us operate vessels safely and efficiently. If environmental stress values are simulated for the ship information derived from satellite data, proper actions can be taken to prevent accidents. Since optical sensor has a high spatial resolution, JERS satellite data are used to track ships and extract their information. We present an algorithm of automatic identification of ship size and velocity. This paper lastly introduce the field testing results of ship detection by RADARSAT SAR imagery, and propose a new approach for a Vessel Monitoring System(VMS), including VTS, and SAR combination service.

• Journal of the Ergonomics Society of Korea
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• v.31 no.3
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• pp.421-426
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• 2012

As headlamp technology advances, newly developed various headlamps were introduced in the market. The objective of this study is to quantitatively analyze the detection distance of the recently developed LED headlamps and existing headlamps, complying with specific technical standard. Background: The detection distance of headlamps is very important to prevent automobile accident at night time. The studies of detection distance of LED, Halogen and HID headlamp have been conducted, but no study has shown the detection distance of pedestrian target with various colors (Black, White, Blue). Method: The experiment of detection distance was conducted with 30 people, which divide into 2 groups as 15 men and 15 women. Automatic transferable target on the rail was manufactured in order to reduce the error of study's result, and ANOVA also conducted to analyze the main effect with sign color, sex and headlamp classified by detection distance. In addition, the luminance by average detection distance was measured as well. Results: The detection distance of headlamps was HID > LED > Halogen. The luminance measure of LED headlamp was lower than HID and Halogen headlamps. Conclusion: The headlamp performs a very significant role for safety at night time but it needs to be improved through assessment of visual characteristics. Also, it needs to be suggested the need of test method for dynamic detection distance concerning technical development is suggested.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.3
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• pp.247-262
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• 2022

In domestic tunnels, it is mandatory to install CCTVs in tunnels longer than 200 m which are also recommended by installation of a CCTV-based automatic accident detection system. In general, the CCTVs in the tunnel are installed at a low height as well as near by the moving vehicles due to the spatial limitation of tunnel structure, so a severe perspective effect takes place in the distance of installed CCTV and moving vehicles. Because of this effect, conventional CCTV-based accident detection systems in tunnel are known in general to be very hard to achieve the performance in detection of unexpected accidents such as stop or reversely moving vehicles, person on the road and fires, especially far from 100 m. Therefore, in this study, the region of interest is set up and a new concept of inverse perspective transformation technique is introduced. Since moving vehicles in the transformed image is enlarged proportionally to the distance from CCTV, it is possible to achieve consistency in object detection and identification of actual speed of moving vehicles in distance. To show this aspect, two datasets in the same conditions are composed with the original and the transformed images of CCTV in tunnel, respectively. A comparison of variation of appearance speed and size of moving vehicles in distance are made. Then, the performances of the object detection in distance are compared with respect to the both trained deep-learning models. As a result, the model case with the transformed images are able to achieve consistent performance in object and accident detections in distance even by 200 m.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06b
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• pp.360-362
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• 2012

교통수단의 발달과 생활수준의 향상으로 도로에 차량이 많이 늘어나고 교통사고가 많이 발생함에 따라, 교통사고 자동인식 시스템에 관한 연구가 많이 진행되고 있다. 본 논문에서는 카메라의 위치에 따라 두 객체의 관심영역 사이의 겹침을 해석하는 것이 달라져 규칙이 변하는 것을 방지하고, 사람의 추론과정과 같이 교통사고를 퍼지 규칙으로 모델링하여 획득한 데이터가 부정확할 경우에 발생하는 잘못된 추론을 보정하기 위한 퍼지 규칙기반 시스템을 제안한다. 카이스트 삼거리에서 촬영한 9개의 사고 시나리오 데이터에 대해 실험하여 DR 87.34%, CDR 89.13%, FAR 10.75%의 결과를 얻었고, 이를 기존의 규칙기반 시스템, 규칙-확률 시스템과 비교하였다.