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

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)
Publication Information
Journal of the Semiconductor & Display Technology / v.21, no.3, 2022 , pp. 63-68 More about this Journal
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
Bone plate defect; Convolution algorithm comparison; Crack detection; Defect detection; Parallel convolution neural networks;
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Times Cited By KSCI : 1  (Citation Analysis)
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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 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.
4 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.
5 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.   DOI
6 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.
7 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.
8 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.   DOI