• The Journal of the Convergence on Culture Technology
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• v.8 no.5
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• pp.735-740
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• 2022

The construction is the industry with the highest fatalities, and the fatalities has not decreased despite various institutional improvements. Accordingly, real-time safety management by applying artificial intelligence (AI) to CCTV images is emerging. Although some research on worker detection by applying AI to images of construction sites is being conducted, there are limitations in performance expression due to problems such as complex background due to the nature of the construction industry. In this study, the YOLO model and the OpenPose model were fused to improve the performance of worker detection and posture estimation to improve the detection performance of workers in various complex conditions. This is expected to be highly useful in terms of unsafe behavior and health management of workers in the future.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.803-804
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• 2023

본 논문은 외국인 근로자의 여권 위변조를 탐지하기 위해 MRZ 코드와 발광 데이터를 활용하는 방법을 제안하고 구현한 것이다. 이 기술은 외국인 근로자의 보호와 국내 안보 강화, 금융 거래의 안전성 향상을 지원하며, 웹 기반 인터페이스를 통해 실시간 판별과 사용자 편의성을 제공한다. 이로써 여권 위변조로 인한 잠재적인 위험을 예방하고 국내 여행 및 비즈니스 환경을 향상시킬 수 있다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.9
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• pp.1259-1265
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• 2022

Recently, AI, big data, and IoT technologies are being used in various solutions such as fire detection and gas or dangerous substance detection for safety accident prevention. According to the status of occupational accidents published by the Ministry of Employment and Labor in 2021, the accident rate, the number of injured, and the number of deaths have increased compared to 2020. In this paper, referring to the dataset construction guidelines provided by the National Intelligence Service Agency(NIA), the dataset is directly collected from the field and learned with YOLOv4 to propose a collision risk object detection system through object detection. The accuracy of the dangerous situation rule violation was 88% indoors and 92% outdoors. Through this system, it is thought that it will be possible to analyze safety accidents that occur in industrial sites in advance and use them to intelligent platforms research.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.11a
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• pp.327-330
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• 2020

Port workers commuting by bicycle are threatened by damaged roads such as port halls created by large cargo. To solve this problem, a device was designed to detect broken roads with sensors and a camera.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.10a
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• pp.928-931
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• 2018

고립된 해상 작업 환경에서의 작업자의 건강상태 관리와 혹시 모를 추락탐지는 작업자의 안전을 위해 매우 중요하다. 본 논문에서는 이러한 고립공간 작업자의 안전을 모니터링하기 위한 헬멧에 부착할 수 있는 IoT 시스템을 위한 알고리즘을 제안한다. 이는 장치가 작업 중에 긴급 상황인지 신속하게 판단할 수 있다. 스마트폰은 누구나 들고 다니기 때문에 사용자 환경은 스마트폰을 이용하여 적용되었다. 작업자들이 착용할 용도이기 때문에 PPG 센서는 불편하지 않도록 귀에 부착한다. PPG 센서를 단독으로 사용하여 스트레스 정도를 파악한다. 3축 가속도 센서는 헬멧에 부착되고 추락을 감지하는데 사용된다. 우리는 여러 센서와 블루투스 통신을 이용하여 발전된 센서 시스템을 만든다. 또한, 우리는 3축 가속도 샘플을 분석하고 정규화하는 알고리즘을 JAVA에서 구현하였다. 스마트 폰을 사용하는 이점은 신호 처리를 위해 별도의 마이크로프로세서(mcu)가 필요하지 않으며 내부 통신 시스템을 통해 제어 센터에 정보를 전송할 수 있다는 것이다.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.3
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• pp.150-159
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• 2022

This paper presents an implementation of detecting whether a helmet is worn and there is a fall accident through individual image analysis in real-time from extracting the image objects of several workers active in the industrial field. In order to detect image objects of workers, YOLO, a deep learning-based computer vision model, was used, and for whether a helmet is worn or not, the extracted images with 5,000 different helmet learning data images were applied. For whether a fall accident occurred, the position of the head was checked using the Pose real-time body tracking algorithm of Mediapipe, and the movement speed was calculated to determine whether the person fell. In addition, to give reliability to the result of a falling accident, a method to infer the posture of an object by obtaining the size of YOLO's bounding box was proposed and implemented. Finally, Telegram API Bot and Firebase DB server were implemented for notification service to administrators.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.7
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• pp.890-895
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• 2021

Recently, the "Act of Serious Disasters, etc" was enacted and institutional and social interest in safety accidents is increasing. In this paper, we analyze statistical data published by government agency on safety accidents that occur in manufacturing sites, and compare various object detection models based on deep learning to build a model to determine dangerous situations to reduce the occurrence of safety accidents. The data-set was directly constructed by collecting images from CCTVs at the manufacturing site, and the YOLO-v4, SSD, CenterNet models were used as training data and evaluation data for learning. As a result, the YOLO-v4 model obtained a value of 81% of mAP. It is meaningful to select a class in an industrial field and directly build a dataset to learn a model, and it is thought that it can be used as an initial research data for a system that determines a risk situation and infers it.

• Journal of the Society of Disaster Information
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• v.20 no.1
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• pp.106-116
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• 2024

Purpose: This paper conducts IoT and CCTV-based safety monitoring to analyze accidents and potential risks occurring at construction sites, and detect and analyze risks such as falls and collisions or abnormalities and to establish a system for early warning using devices like a walkie-talkie and chatbot service. Method: A safety management service model is presented through smart construction technology case studies at the construction site and review a relevant literature analysis. Result: According to 'Construction Accident Statistics,' in 2021, there were 26,888 casualties in the construction industry, accounting for 26.3% of all reported accidents. Fatalities in construction-related accidents amounted to 417 individuals, representing 50.5% of all industrial accident-related deaths. This study suggests implementing AI chatbot services for construction site safety management utilizing IoT-based health monitoring technologies in smart construction practices. Construction sites where stakeholders such as workers participate were demonstrated by implementing an artificial intelligence chatbot system by selecting major risk areas within the workplace, such as scaffolding processes, openings, and access to hazardous machinery. Conclusion: The possibility of commercialization was confirmed by receiving more than 90 points in the satisfaction survey of participating workers regarding the empirical results of the artificial intelligence chatbot service at construction sites.

• Journal of Labour Economics
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• v.37 no.1
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• pp.87-116
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• 2014

Forecasting of short-term employment fluctuations provides a useful tool for policy makers in risk managing the labor market. Following the process of producing the composite leading indicator for macro economy, the paper develops the employment leading indicator(ELI) for the purpose of short-term forecasting non-farm payroll employment in private sectors. ELI focuses on early detecting the point of time and the speed in phase change of employment level.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.9
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• pp.1159-1164
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• 2018

Recently, Internet use is increasing in various fields. Especially in the sudden disaster area, the role of Internet of things that can continuously monitor is getting bigger. In this study, the characteristics of the confined space and the environmental hazards are examined, and the Internet of the object which is being commercialized will be reviewed. Accidents in confined spaces are very high compared to other places, and it is very difficult to predict accidents. Recently, various attempts have been made to prevent accidents in confined spaces using the Internet of things. Especially, it detects the various gases that can occur in the closed space using sensors and sends them to the workers in real time, so that the risk can be detected in advance to minimize the risk. In this paper, we propose an effective disaster prevention plan using the Internet of things through the case study of the Internet for the prevention of accidents in a confined space.