한국전자통신학회논문지 (The Journal of the Korea institute of electronic communication sciences)

한국전자통신학회 (Korea Institute of Electronic Communication Science)
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딥러닝 기반 직원 안전용 헬멧과 마스크 분류

Helmet and Mask Classification for Personnel Safety Using a Deep Learning

  • ;
  • 김강철 (전남대학교 전기컴퓨터공학부)
  • 투고 : 2022.05.11
  • 심사 : 2022.06.17
  • 발행 : 2022.06.30
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⟨ 이전 논문 다음 논문 ⟩

초록

코로나 시대에서 감염의 위험을 줄이기 위하여 반드시 마스크를 착용하여야 하며, 건축 공사장과 같은 위험한 작업 환경에서 일하는 직원의 안전을 위하여 헬맷을 쓰는 것은 필수불가결하다. 본 논문에서는 헬멧과 마스크의 착용 여부를 분류하는 효과적인 딥러닝 모델 HelmetMask-Net를 제안한다. HelmetMask-Net은 CNN 기반으로 설계되며, 전처리, 컨벌류션 계층, 맥스풀링 계층과 4 가지 출력이 있는 완전결합 계층으로 구성되며, 헬멧, 마스크, 헬멧과 마스크, 헬멧과 마스크을 착용하지 않은 4 가지 경우를 구분한다. 정확도, 최적화, 초월 변수의 수를 고려한 실험으로 2 컨볼루션 계층과 AdaGrad 최적화를 가진 구조가 선정되었다. 모의 실험 결과 99%의 정확도를 보여 주었고, 기존의 모델에 비하여 성능이 우수함을 확인하였다. 제안된 분류기는 코비드 19 시대에 직원의 안전을 향상시킬 수 있을 것이다.

Wearing a mask is also necessary to limit the risk of infection in today's era of COVID-19 and wearing a helmet is inevitable for the safety of personnel who works in a dangerous working environment such as construction sites. This paper proposes an effective deep learning model, HelmetMask-Net, to classify both Helmet and Mask. The proposed HelmetMask-Net is based on CNN which consists of data processing, convolution layers, max pooling layers and fully connected layers with four output classifications, and 4 classes for Helmet, Mask, Helmet & Mask, and no Helmet & no Mask are classified. The proposed HelmatMask-Net has been chosen with 2 convolutional layers and AdaGrad optimizer by various simulations for accuracy, optimizer and the number of hyperparameters. Simulation results show the accuracy of 99% and the best performance compared to other models. The results of this paper would enhance the safety of personnel in this era of COVID-19.

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참고문헌

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