Journal of the Korean Society of Safety (한국안전학회지)

The Korean Society of Safety (한국안전학회)
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Application of Deep Learning Algorithm for Detecting Construction Workers Wearing Safety Helmet Using Computer Vision

건설현장 근로자의 안전모 착용 여부 검출을 위한 컴퓨터 비전 기반 딥러닝 알고리즘의 적용

  • Kim, Myung Ho (Department of Safety Engineering, Pukyong National University) ;
  • Shin, Sung Woo (Department of Safety Engineering, Pukyong National University) ;
  • Suh, Yong Yoon (Department of Safety Engineering, Pukyong National University)
  • Received : 2019.08.22
  • Accepted : 2019.09.28
  • Published : 2019.12.31
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Abstract

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.

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References

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