• Journal of the Korea Institute of Building Construction
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• v.23 no.1
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• pp.81-92
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• 2023

According to the 2020 industrial accident reports of the Ministry of Employment and Labor, the number of fatal accidents in the construction industry over the past 5 years has been higher than in other industries. Of these more than 50% of fatal accidents are initially caused by fall accidents. The central government is intensively managing falling/jamming protection device and the use of personal protective equipment to eradicate the inappropriate factors disrupting safety at construction sites. In addition, although efforts have been made to prevent safety accidents with the proposal of the Special Act on Construction Safety, fatalities on construction sites are constantly occurring. Therefore, this study developed a model that automatically detects the wearing state of the worker's safety helmet and belt using computer vision technology. In considerations of conditions occurring at construction sites, we suggest an optimization method, which has been verified in terms of the accuracy and operation speed of the proposed model. As a result, it is possible to improve the efficiency of inspection and patrol by construction site managers, which is expected to contribute to reinforcing competency of safety management.

• Journal of the Institute of Convergence Signal Processing
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• v.17 no.1
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• pp.18-25
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• 2016

As more people have cars, the threat of traffic accidents is posed on men and women of all ages. The main culprit of traffic accidents is driving while intoxicated or drowsy. The method to recognize and prevent the cause of traffic accidents is to use lane detection. In this study, a total of 4,000 frames (day image: 2,900 frames, night image: 1,100 frames) were used to test lane detection. According to the test, in the case of day image, when the threshold of Sobel edge detection technique was detected with second-order differential equation, there was the highest candidate lane detection rate which was 86.1%. In the threshold of Canny edge detection technique, the highest detection rate of 88.0% was found at Low=50, and High=300. In the case of night image, the threshold of Sobel edge detection technique, when horizontal calculation and vertical calculation had second-order differential equation, and when horizontal-vertical calculation had 1.5th-order differential equation, there was the highest detection rate which was 83.1%. In the threshold of Canny edge detection technique, the highest detection rate of 89.9% was found at Low=50, and High=300.

• Journal of Applied Reliability
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• v.14 no.3
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• pp.191-196
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• 2014

The importance of railway safety has become increasingly significant domestically as well as internationally, as a series of high speed railway accidents and other major accidents have occurred recently. Especially for the domestic railway, the Korean Railway Safety Law has been revised recently, mandates all the domestic railway operation authorities to render the performance of RAMS and RCM. This study inspects and analyzes the current status of the sensing technology of the electric detection system to tell the status of railway facilities in the highway railway in a real time through a sensor. It also performs the reliability analysis of the electric detection system that is being progressed as a study assignment and suggests the system construction for the higher reliability.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.871-876
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• 2022

In the construction industry, fatal accidents related to scaffolds frequently occur. To prevent such accidents, scaffolds should be carefully monitored for their safety status. However, manual observation of scaffolds is time-consuming and labor-intensive. This paper proposes a method that automatically analyzes the installation status of scaffold joints based on images acquired from a Unmanned Aerial Vehicle (UAV). Using a deep learning-based object detection algorithm (YOLOv5), scaffold joints and joint components are detected. Based on the detection result, a two-stage rule-based classifier is used to analyze the joint installation status. Experimental results show that joints can be classified as safe or unsafe with 98.2 % and 85.7 % F1-scores, respectively. These results indicate that the proposed method can effectively analyze the joint installation status in UAV-acquired scaffold images.

• Journal of Digital Policy
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• v.2 no.4
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• pp.33-39
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• 2023

With a continuous occurrence of right-turn traffic accidents at intersections, there is an increasing demand for measures to address these incidents. In response, a technology has been developed to detect the presence of pedestrians through object detection in CCTV footage at right-turn areas and display warning messages on the screen to alert drivers. The YOLO (You Only Look Once) model, a type of object detection model, was employed to assess the performance of object detection. An algorithm was also devised to address misidentification issues and generate warning messages when pedestrians are detected. The accuracy of recognizing pedestrians or objects and outputting warning messages was measured at approximately 82%, suggesting a potential contribution to preventing right-turn accidents

• Journal of the Korea Society of Computer and Information
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• v.22 no.12
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• pp.79-85
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• 2017

In this paper, we propose a CNN-based swimmer detection algorithm. Every year, water safety accidents have been occurred frequently, and accordingly, intelligent video surveillance systems are being developed to prevent accidents. Intelligent video surveillance system is a real-time system that detects objects which users want to do. It classifies or detects objects in real-time using algorithms such as GMM (Gaussian Mixture Model), HOG (Histogram of Oriented Gradients), and SVM (Support Vector Machine). However, HOG has a problem that it cannot accurately detect the swimmer in a complex and dynamic environment such as a beach. In other words, there are many false positives that detect swimmers as waves and false negatives that detect waves as swimmers. To solve this problem, in this paper, we propose a swimmer detection algorithm using CNN (Convolutional Neural Network), specialized for small object sizes, in order to detect dynamic objects and swimmers more accurately and efficiently in complex environment. The proposed CNN sets the size of the input image and the size of the filter used in the convolution operation according to the size of objects. In addition, the aspect ratio of the input is adjusted according to the ratio of detected objects. As a result, experimental results show that the proposed CNN-based swimmer detection method performs better than conventional techniques.

• Journal of Drive and Control
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• v.19 no.3
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• pp.23-31
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• 2022

Traffic safety issues have recently been seriously magnified, due to child deaths in apartment complexes and parking lots. Accordingly, traffic safety technologies are being developed to recognize dangerous situations on living roads and to provide warning services. In this study, a collision detection algorithm was developed to prevent collision accidents between moving objects, by using object type and location information provided from CCTV monitoring devices. To determine the exact collision between moving objects, an object movement model was developed to predict the range of movement by considering the moving characteristics of the object, and a collision detection algorithm was developed to efficiently analyze the presence and location of the collision. The developed object movement model as well as the collision detection algorithm were simulated, in a virtual space of an actual living road to verify performance and derive supplementary matters.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.6
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• pp.1365-1372
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• 2018

Recently, the aging of agricultural society has caused a rapid increase in safety accidents in -agricultural machinery. Sometimes, the wounded may be left unattended resulting serious situation. In order to solve these problems, ICT technology is used to detect and inform the accident quickly when a safety accident such as overturning, collision or other accidents occurs during the operation or moving the agricultural machinery, such as cultivator, rearing machine, and tractor. A system capable of minimizing the amount of data is required. In this paper, an ICT - based automatic accident detection & propagation system is proposed for the agricultural machinery accident such as colliding crashes or overturning of agricultural machinery. The proposed system enables quick rescue by sending a text message automatically to family, acquaintances, hospitals and 119 in the event of an agricultural accident.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.11
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• pp.2423-2428
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• 2010

The number of motor vehicle registrations in Korea is increasing steadily each year, driven by industry development and economic growth. The number of traffic accidents is also rapidly increasing. Korea has a relatively high number of traffic accidents among OECD member countries, and it ranks among the highest in traffic accident death rates. This death rate is higher compared to death rates as a proportion of the number of traffic accidents in each country. It is very common for drivers to lose consciousness in traffic collisions, which leads to a failure to carry out early emergency measures. In order to prevent such situations as well as hit-and-runs and people left uncared for after traffic accidents, there is a need for motor vehicle black boxes and accident report systems. This study addressed the need for an emergency evacuation system for people injured in traffic accidents and a secondary traffic accident prevention system by developing a motor vehicle emergency situation detection and report system combined with a black box, and materializing it as an actual system.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.10a
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• pp.200-202
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• 2010

The number of motor vehicle registrations in Korea is increasing steadily each year, driven by industry development and economic growth. The number of traffic accidents is also rapidly increasing. Korea has a relatively high number of traffic accidents among OECD member countries, and it ranks among the highest in traffic accident death rates. This death rate is higher compared to death rates as a proportion of the number of traffic accidents in each country. It is very common for drivers to lose consciousness in traffic collisions, which leads to a failure to carry out early emergency measures. In order to prevent such situations as well as hit-and-runs and people left uncared for after traffic accidents, there is a need for motor vehicle black boxes and accident report systems. This study addressed the need for an emergency evacuation system for people injured in traffic accidents and a secondary traffic accident prevention system by developing a motor vehicle emergency situation detection and report system combined with a black box, and materializing it as an actual system.