Earticle

초록

영어

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.

목차

Abstract
1. INTRODUCTION
2. RELATED WORKS
3. METHODOLGY
3.1 YOLOv5[10]
3.2 TRANSFER LEARNING
4. EXPERIMENTS AND ANALYSIS
4.1 Experimetal environments and datasets
4.2 Evaluation method
4.3 Experimental result analysis
5. CONCLUSION
REFERENCES

키워드

저자

• NaeJoung Kwak [ Prof., Dept. of Cyber and Security, Baejae Univ., Korea ]
• DongJu Kim [ Professor, Postech Institute of Artificial Intelligence, POSTECH ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Advanced Culture Technology(IJACT)

• 간기

  계간

• pISSN

  2288-7202

• eISSN

  2288-7318

• 수록기간

  2013~2025

• 등재여부

  KCI 등재

• 십진분류

  KDC 600 DDC 700