International journal of advanced smart convergence

  • 제10권4호
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  • Pages.110-116
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  • 2021
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  • 2288-2847(pISSN)
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  • 2288-2855(eISSN)
한국인터넷방송통신학회 (The Institute of Internet, Broadcasting and Communication)
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Real-Time Earlobe Detection System on the Web

  • Kim, Jaeseung (Department of Plasma Bio Display, Kwangwoon University) ;
  • Choi, Seyun (Department of Smartsystem, Kwangwoon University) ;
  • Lee, Seunghyun (Ingenium College Liberal Arts, Kwangwoon University) ;
  • Kwon, Soonchul (Graduate School of Smart Convergence, Kwangwoon University)
  • 투고 : 2021.10.17
  • 심사 : 2021.10.25
  • 발행 : 2021.12.31
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초록

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 real-time 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.

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과제정보

This research is supported by Ministry of Culture, Sports and Tourism and Korea Creative Content Agency(Project Number: R2021040083)

참고문헌

  1. K. Kim, "Virtual Livestreamed Performance and E-License," International journal of advanced smart convergence, vol. 9, no. 3, pp. 78-84, Sep. 2020. DOI: 10.7236/IJASC.2020.9.3.78
  2. V. Bazarevsky, Y. Kartynnik, A. Vakunov, K. Raveendran, and M. Grundmann, "Sub-millisecond neural face detection on mobile gpus," arXiv preprint arXiv:1907.05047, 2019.
  3. P. Chen, Y. Dang, R. Liang, W. Zhu and X. He, "Real-Time Object Tracking on a Drone With Multi-Inertial Sensing Data," in IEEE Transactions on Intelligent Transportation Systems, vol. 19, no. 1, pp. 131-139, Jan 2018. DOI: 10.1109/TITS.2017.2750091
  4. S. Xiong, S. Li, L. Kou, W. Guo, Z. Zhou, and Z. Zhao, "Td-VOS: Tracking-Driven Single-Object Video Object Segmentation," 2020 IEEE 5th International Conference on Image, Vision and Computing (ICIVC), pp. 102-107, 2020. DOI: 10.1109/ICIVC50857.2020.9177471
  5. J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, "You only look once: Unified, real-time object detection," in IEEE conference, pp.779-788, June 2016. DOI: 10.1109/CVPR.2016.91
  6. J. Redmon, A. Farhadi, "YOLO9000: better, faster, stronger," in IEEE conference, pp.6517-6525, July 2017. DOI: 10.1109/CVPR.2017.690
  7. J. Redmon, A. Farhadi, "Yolov3: An incremental improvement," in arXiv, April 2018.
  8. A. Bochkovskiy, Wang Chien-Yao and Liao, and Hong-Yuan Mark, "Yolov4: Optimal speed and accuracy of object detection," in arXiv, April 2020. DOI: 10.1109/CVPR.2016.91
  9. R. Xu, H. Lin, K. Lu, L. Cao, Y. Liu, "A Forest Fire Detection System Based on Ensemble Learning," in Forests, vol.12, no.2, pp.217, Feb 2021. DOI: 10.3390/f12020217
  10. C. Y. Wang, H. Y. M. Liao, Y. H. Wu, P. Y. Chen, J. W. Hsieh, and I. H. Yeh , "CSPNet: A new backbone that can enhance learning capability of CNN," in IEEE/CVF conference, pp.1571-1580, June 2020. DOI: 10.1109/CVPRW50498.2020.00203
  11. Y. Zhou, and S. Zaferiou, "Deformable models of ears in-the-wild for alignment and recognition. In 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition," pp. 626-633, 2017.
  12. Y. Kartynnik, A. Ablavatski, I. Grishchenko, and M. Grundmann, "Real-time facial surface geometry from monocular video on mobile GPUs," arXiv preprint arXiv:1907.06724, 2019.