Earticle

초록

영어

This paper proposed a real-time earlobe detection system using deep learning on the web. Existing deep learning-based detection methods often find independent objects such as cars, mugs, cats, and people. We proposed a way to receive an image through the camera of the user device in a web environment and detect the earlobe on the server. First, we took a picture of the user's face with the user's device camera on the web so that the user's ears were visible. After that, we sent the photographed user's face to the server to find the earlobe. Based on the detected results, we printed an earring model on the user's earlobe on the web. We trained an existing YOLO v5 model using a dataset of about 200 that created a bounding box on the earlobe. We estimated the position of the earlobe through a trained deep learning model. Through this process, we proposed a realtime earlobe detection system on the web. The proposed method showed the performance of detecting earlobes in real-time and loading 3D models from the web in real-time.

목차

Abstract
1. Introduction
2. Background Theory
2.1. YOLOv5
3. Proposed Model
4. Experiments and Results
5. Conclusion
Acknowledgement
References

키워드

저자

• Jaeseung Kim [ M.S., Department of Plasma Bio Display, Kwangwoon University, South Korea ]
• Seyun Choi [ M.S., Department of Smartsystem, Kwangwoon University, South Korea ]
• Seunghyun Lee [ Professor, Ingenium College Liberal Arts, Kwangwoon University, South Korea ]
• Soonchul Kwon [ Associate professor, Graduate School of Smart Convergence, Kwangwoon University, Seoul, Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  The International Journal of Advanced Smart Convergence

• 간기

  계간

• pISSN

  2288-2847

• eISSN

  2288-2855

• 수록기간

  2012~2025

• 십진분류

  KDC 326 DDC 380