• Proceedings of the Korean Institute of Building Construction Conference
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• 2023.05a
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• pp.173-174
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• 2023

In this paper, we introduce a model for detecting Personal Protective Equipment (PPE) using YOLO (You Only Look Once), an object detection neural network. PPE is used to maintain a safe working environment, and proper use of PPE protects workers' safety and health. However, failure to wear PPE or wearing it improperly can cause serious safety issues. Therefore, a PPE detection system is crucial in industrial settings.

• The Korean Journal of Community Living Science
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• v.28 no.3
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• pp.391-401
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• 2017

This study was conducted to evaluate the relationships between exposure level to pyrethroid pesticide and wearing of protective equipment in 194 Chung-nam orchard male farmers. The urinary metabolites of pyrethroid pesticides, including Cis, Trans, DBCA, and 3-PBA, were analyzed by GC/MSD. As a result of this study, the detection rate and exposure level of 3-PBA was the highest among pyrethroid metabolites discovered by orchard farmers. As a result of analyzing the actual conditions of wearing protective equipment by the subjects of this study, the rate of agricultural farmers who wore four pieces of protective equipment compared to agricultural farmers wearing a single piece of protective clothing was as high as 35.1%. Pyrethroid exposure levels were low when farmers wore more personal protective equipment (PPE). In conclusion, training with regards to pesticide hazards and protective equipment for farmers who spray pesticides will help reduce pesticide exposure levels.

• International Journal of Advanced Culture Technology
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• v.10 no.1
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• pp.302-309
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• 2022

Occupational safety accidents are caused by various factors, and it is difficult to predict when and why they occur, and it is directly related to the lives of workers, so the interest in safety accidents is increasing every year. Therefore, in order to reduce safety accidents at industrial fields, workers are required to wear personal protective equipment. In this paper, we proposes a method to automatically check whether workers are wearing safety helmets among the protective equipment in the industrial field. It detects whether or not the helmet is worn using YOLOv5, a computer vision-based deep learning object detection algorithm. We transfer learning the s model among Yolov5 models with different learning rates and epochs, evaluate the performance, and select the optimal model. The selected model showed a performance of 0.959 mAP.

• Journal of the Society of Disaster Information
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• v.18 no.2
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• pp.269-279
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• 2022

Purpose: The purpose of this study is to develop a deep learning-based personal protective equipment detection model for disaster prevention at construction sites, and to apply it to actual construction sites and to analyze the results. Method: In the method of conducting this study, the dataset on the real environment was constructed and the developed personal protective equipment(PPE) detection model was applied. The PPE detection model mainly consists of worker detection and PPE classification model.The worker detection model uses a deep learning-based algorithm to build a dataset obtained from the actual field to learn and detect workers, and the PPE classification model applies the PPE detection algorithm learned from the worker detection area extracted from the work detection model. For verification of the proposed model, experimental results were derived from data obtained from three construction sites. Results: The application of the PPE recognition model to construction site brings up the problems related to mis-recognition and non-recognition. Conclusions: The analysis outcomes were produced to apply the object recognition technology to a construction site, and the need for follow-up research was suggested through representative cases of worker recognition and non-recognition, and mis-recognition of personal protective equipment.

• 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.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.313-316
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• 2015

Construction is one of the most dangerous job sectors, which reports tens of thousands of time-loss injuries and deaths every year. These disasters incur delays and additional costs to the projects. The safety management needs to be on the top primary tasks throughout the construction to avoid fatal accidents and to foster safe working environments. One of the safety regulations that are frequently violated is the wearing of personal protection equipment (PPE). In order to facilitate monitoring of the compliance of the PPE wearing regulations, this paper proposes a vision based method that automatically identifies whether workers wear hard hats and safety vests. The method involves three modules - human body detection, identification of safety vest wearing, and hard hat detection. First, human bodies are detected in the video frames captured by real-time on-site construction cameras. The detected human bodies are classified into with/without wearing safety vests based on the color features of their upper parts. Finally, hard hats are detected on the nearby regions of the detected human bodies and the locations of the detected hard hats and human bodies are correlated to reveal their corresponding matches. In this way, the proposed method provides any appearance of the workers without wearing hard hats or safety vests. The method has been tested on onsite videos and the results signify its potential to facilitate site safety monitoring.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.721-727
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• 2022

Construction safety remains an ongoing concern, and project managers have been increasingly forced to cope with myriad uncertainties related to human operations on construction sites and the lack of a skilled workforce in hazardous circumstances. Various construction fatality monitoring systems have been widely proposed as alternatives to overcome these difficulties and to improve safety management performance. In this study, we propose an intelligent, automatic control system that can proactively protect workers using both the analysis of big data of past safety accidents, as well as the real-time detection of worker non-compliance in using personal protective equipment (PPE) on a construction site. These data are obtained using computer vision technology and data analytics, which are integrated and reinforced by lessons learned from the analysis of big data of safety accidents that occurred in the last 10 years. The system offers data-informed recommendations for high-risk workers, and proactively eliminates the possibility of safety accidents. As an illustrative case, we selected a pilot project and applied the proposed system to workers in uncontrolled environments. Decreases in workers PPE non-compliance rates, improvements in variable compliance rates, reductions in severe fatalities through guidelines that are customized according to the worker, and accelerations in safety performance achievements are expected.

• The Korean Journal of Pesticide Science
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• v.20 no.2
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• pp.172-180
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• 2016

This study was conducted to evaluate the relations between the exposure level of organophosphates (OPs) pesticide and application factors among rural farmers. The urinary dialkylphosphates, metabolites of organophosphorus pesticides, including DMP, DEP, DMTP and DETP were analyzed by GC/MSD and GC/MS/MS. The DMP and DMTP were detected more in the use of a speed sprayer without cap than with a capped one. Also, the less farmers wore the personal protective equipment (PPE), the more these were detected. The amount of organophosphorus exposure was the highest in the use of a power sprayer. However, it was low when a farmer applied pesticides with a speed sprayer with cap and wore more PPE. In this study, the detection rate was analyzed by chi-square test, the exposure level of OPs was analyzed by a generalized linear model.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.07a
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• pp.650-651
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• 2020

본 논문은 YOLO v4 알고리즘을 이용하여 산업 현장에서 근로자의 개인 보호장비를 검출하는 방법을 제시한다. 학습데이터 주석은 사람 영역, 안전모, 안전 조끼 혹은 벨트 영역을 검출하도록 처리하였으며, 학습데이터 2,198개, 검증데이터 275개를 학습하는 데 이용하였다. 실험 결과 학습 반복 수 10,000번을 기준으로 81.81%의 mAP가 나옴을 확인하였다. 추후 정확도 개선을 위해 학습데이터 구축 및 전·후처리 알고리즘 관련 연구를 수행할 예정이다.

• Safety and Health at Work
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• v.11 no.4
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• pp.537-542
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• 2020

Background: Personal protective equipment (PPE) has been designed in such a way to reduce accident rates. Unfortunately, existing PPE is rather ineffective as it is not able to provide warning signals when hazard is around. The integration of intelligent systems is envisaged to increase the efficiency of existing PPE. Methods: This project designed a safety vest incorporated with metal detectors which can provide immediate warning to the field workers when there is metal hazard around. This product has greater freedom of design via smart manufacturing as it involves the assembly of few commercially available parts into a single entity. Briefly, the metal detector is a do it yourself (DIY) kit, and the safety vest is purchasable from any local market. The DIY kit was connected to a copper coil and being sewed into the safety vest. Results: The metal detector induces beeping sound when there is metal hazard around. A total of 121 engineering students were introduced to the prototype before being requested to answer a survey associated with the design. Respondents have rated >3.00/5.00 for the design simplicity, ease of usage, and light weight. Meanwhile, respondents suggested that the design should be further improved by increasing the metal detection range. Conclusion: It is envisaged that the introduction of this smart safety vest will allow the workers to carry out their duties securely by reducing the accident rates. Particularly, such design is expected to reduce workplace accident especially during night time at construction sites where the visibility is low.