Automatic Safety Monitoring of Construction Hazard Working Zone

Yu, Wen-der; Liao, Hsien-Chou; Hsiao, Wen-Ta; Chang, Hsien-Kuan; Tsai, Chi-Kong; Lin, Cheng‐Hsiung

doi:10.1145/3412953.3412969

Cited by 5 publications

(1 citation statement)

References 16 publications

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“…Thanks to the understanding and semantic segmentation of videos, important studies are carried out on occupational health and safety (Yu et al, 2020). In a research, it was stated that approximately 90% of all accidents that occur in work environments are caused by unsafe behaviors (Heinrich & Granniss, 1959).…”

Section: Introductionmentioning

confidence: 99%

Object Detection for Safe Working Environments using YOLOv4 Deep Learning Model

Önal¹,

Dandıl²

2021

European Journal of Science and Technology

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The health and safety of employees in workplaces maintains its importance since the concept of production emerged. Recent developments in computer vision and deep learning have made it widespread to be used in work environments as a secondary tool in ensuring occupational safety from surveillance videos. Thus, an important performance is achieved by minimizing human-induced errors in working environments. In this study, a method based on the YOLOv4 deep learning model is proposed to control the use of personal protective equipment from videos and to detect unsafe movements in the working environments of facilities operating in the field of industrial production. In the study, a dataset is created with videos collected from different working environments. In the study, later, on the prepared video dataset, the detection of personal protective equipment such as helmets, vests, masks, gloves, eyeglasses used by workers in factories operating in industrial areas and whether they use the appropriate equipment correctly is determined using the YOLOv4 framework. In the experimental studies conducted within the scope of the study, the mean average precision (mAP) value is achieved as 91.18% as a result of the training performed in the YOLOv4 network. In addition, results of 0.89, 0.91, 0.90, 70.35 and 1.1147 are obtained for other measurement metrics such as precision, recall, F1-score, intersection over union (IoU), and average loss, respectively. As a result, in the proposed study, instant inspection of the videos collected from the cameras installed in the factories, the meaning of the scene and the control of safe working environments are successfully achieved.

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Section: Introductionmentioning

confidence: 99%

Object Detection for Safe Working Environments using YOLOv4 Deep Learning Model

Önal¹,

Dandıl²

2021

European Journal of Science and Technology

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Innovations in safety management for construction sites: the role of deep learning and computer vision techniques

Mohy,

Bassioni,

Elgendi

et al. 2024

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Purpose The purpose of this study is to investigate the potential of using computer vision and deep learning (DL) techniques for improving safety on construction sites. It provides an overview of the current state of research in the field of construction site safety (CSS) management using these technologies. Specifically, the study focuses on identifying hazards and monitoring the usage of personal protective equipment (PPE) on construction sites. The findings highlight the potential of computer vision and DL to enhance safety management in the construction industry. Design/methodology/approach The study involves a scientometric analysis of the current direction for using computer vision and DL for CSS management. The analysis reviews relevant studies, their methods, results and limitations, providing insights into the state of research in this area. Findings The study finds that computer vision and DL techniques can be effective for enhancing safety management in the construction industry. The potential of these technologies is specifically highlighted for identifying hazards and monitoring PPE usage on construction sites. The findings suggest that the use of these technologies can significantly reduce accidents and injuries on construction sites. Originality/value This study provides valuable insights into the potential of computer vision and DL techniques for improving safety management in the construction industry. The findings can help construction companies adopt innovative technologies to reduce the number of accidents and injuries on construction sites. The study also identifies areas for future research in this field, highlighting the need for further investigation into the use of these technologies for CSS management.

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