Earticle

초록

영어

This study is to select an optimal object detection algorithm for designing a self-checkout counter to improve the inconvenience of payment systems for products without existing barcodes. To this end, a performance comparison analysis of YOLO v2, Tiny YOLO v2, and the latest YOLO v5 among deep learning-based object detection algorithms was performed to derive results. In this paper, performance comparison was conducted by forming learning data as an example of 'donut' in a bakery store, and the performance result of YOLO v5 was the highest at 96.9% of mAP. Therefore, YOLO v5 was selected as the artificial intelligence object detection algorithm to be applied in this paper. As a result of performance analysis, when the optimal threshold was set for each donut, the precision and reproduction rate of all donuts exceeded 0.85, and the majority of donuts showed excellent recognition performance of 0.90 or more. We expect that the results of this paper will be helpful as the fundamental data for the development of an automatic payment system using AI self-service technology that is highly usable in the non-face-to-face era.

목차

Abstract
1. INTRODUCTION
2. RELATED RESEARCH
2.1 Object detection
2.2 YOLO v2
2.3 Tiny YOLO v2
2.4 YOLO v5
3. ALGORITHM EVALUATION RESULTS
4. COMPARISON OF ALGORITHM RESULTS
5. CONCLUSION
REFERENCES

키워드

저자

• Heeyoung Kim [ Doctoral Course, Department of Immersive Content Convergence, General graduate school, Kwangwoon University, Korea ]
• Dongmin Kim [ CEO, JLK Inc., Korea ]
• Gihwan Ryu [ Professor, Department of Tourism Industry, Graduate school of smart convergence, Kwangwoon University, Korea ]
• Hotak Hong [ Researcher, AI R&D Center, JLK Inc., Korea ] 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