Journal of the Semiconductor & Display Technology (반도체디스플레이기술학회지)

The Korean Society Of Semiconductor & Display Technology (한국반도체디스플레이기술학회)
권호 표지

A Study on Detection Performance Comparison of Bone Plates Using Parallel Convolution Neural Networks

병렬형 합성곱 신경망을 이용한 골절합용 판의 탐지 성능 비교에 관한 연구

  • Lee, Song Yeon (Department of Mechatronics Engineering, Graduate School of Korea University of Technology and Education) ;
  • Huh, Yong Jeong (School of Mechatronics Engineering, Korea University of Technology and Education)
  • 이송연 (한국기술교육대학교 대학원 메카트로닉스공학과) ;
  • 허용정 (한국기술교육대학교 메카트로닉스공학부)
  • Received : 2022.09.02
  • Accepted : 2022.09.21
  • Published : 2022.09.30
Download PDF
⟨ Previous Next ⟩

Abstract

In this study, we produced defect detection models using parallel convolution neural networks. If convolution neural networks are constructed parallel type, the model's detection accuracy will increase and detection time will decrease. We produced parallel-type defect detection models using 4 types of convolutional algorithms. The performance of models was evaluated using evaluation indicators. The model's performance is detection accuracy and detection time. We compared the performance of each parallel model. The detection accuracy of the model using AlexNet is 97 % and the detection time is 0.3 seconds. We confirmed that when AlexNet algorithm is constructed parallel type, the model has the highest performance.

Keywords

Acknowledgement

이 논문은 2022년도 한국기술교육대학교 교수 교육연구진흥과제 지원에 의하여 연구되었음.

References

  1. Dong-Hyun Go, Ji-Hye You, Eun-Kyo Kim, A-Hyeon Gyeon, Chun-Il Lim, Kyoung-Won Seo and Mi-Jeong Kim, "A Study on the Development of Performance Evaluation Guideline for Bone Plates in Maxillofacial Surgery," J.of The Korean Society of Food, Drug and Cosmetic Regulatory Sciences, Vol. 13, pp. 133-141, 2018.
  2. Song-Yeon Lee and Yong-Jeong Huh, "A Study on Surface Defect Detection Model of 3D Printing Bone Plate Using Deep Learning Algorithm", J. of The Korean Society of Semiconductor & Display Technology, Vol.20, pp. 68-73, 2021.
  3. Gee-Yeun Kim and Hyoung-Gook Kim, "Performanc Comparison of Lung Sound Classification Using Various Convolutional Neural Networks," J. of The Acoustical Society of Korea, Vol. 38, pp. 568-573, 2019. https://doi.org/10.7776/ASK.2019.38.5.568
  4. Chang-Hee Yang, Kyu-Sub Park, Young-Seop Kim and Yong-Hwan Lee, "Comparative Analysis for Emotion Expression Using Three Methods Based by CNN", J. of The Korean Society of Semiconductor & Display Technology, Vol.19, pp. 65-70, 2020.
  5. Jun-Hee Jung and Joong-Hwee Cho, "A Through-focus Scanning Optical Microscopy Dimensional Measurement Method based on A Deep-learning Regression Model", J. of The Korean Society of Semiconductor & Display Technology, Vol.21, pp. 108-113, 2022.
  6. Se-Rang Oh and Young-Chul Bae, "Braille Block Recognition Algorithm for the Visually Impaired Based on YOLO V3", J. of The Korean Institute of Intelligent Systems, Vol.31, pp. 60-67, 2021. https://doi.org/10.5391/JKIIS.2021.31.1.060
  7. Song-Yeon Lee and Yong-Jeong Huh, "A Study on Shape Warpage Defect Detection Medel of Scaffold Using Deep Learning Base CNN", J. of The Korean Society of Semiconductor & Display Technology, Vol.20, pp. 99-103, 2021.
  8. Ji-Soo Kang, Se-Eun Shim, Sun-Moon Jo and Kyung-Yong Chung, "YOLO based Light Source Object Detection for Traffic Image Big Data Processing", J. of The Korean Convergence for Information Technology, Vol.10, pp. 40-46, 2020.