Journal of the Korea Institute of Information and Communication Engineering (한국정보통신학회논문지)

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
권호 표지
DOI QR코드

DOI QR Code

Machine Parts(O-Ring) Defect Detection Using Adaptive Binarization and Convex Hull Method Based on Deep Learning

적응형 이진화와 컨벡스 헐 기법을 적용한 심층학습 기반 기계부품(오링) 불량 판별

  • Kim, Hyun-Tae (Major of Applied Software Engineering, Dongeui University,) ;
  • Seong, Eun-San (Department of Digital Media Engineering, Dong-Eui University)
  • Received : 2021.10.06
  • Accepted : 2021.10.28
  • Published : 2021.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

O-rings fill the gaps between mechanical parts. Until now, the sorting of defective products has been performed visually and manually, so classification errors often occur. Therefore, a camera-based defect classification system without human intervention is required. However, a binarization process is required to separate the required region from the background in the camera input image. In this paper, an adaptive binarization technique that considers the surrounding pixel values is applied to solve the problem that single-threshold binarization is difficult to apply due to factors such as changes in ambient lighting or reflections. In addition, the convex hull technique is also applied to compensate for the missing pixel part. And the learning model to be applied to the separated region applies the residual error-based deep learning neural network model, which is advantageous when the defective characteristic is non-linear. It is suggested that the proposed system through experiments can be applied to the automation of O-ring defect detection.

오링은 기계 부품들 사이에서 틈을 메워주는 역할을 한다. 지금까지 불량품 선별은 육안 및 수작업으로 수행하여 분류 오류가 자주 발생한다. 따라서 사람의 개입이 없는 카메라 기반의 불량품 분류 시스템이 필요하다. 그러나 카메라 입력 영상에서 배경으로부터 필요 영역을 분리하기 위해 이진화 과정이 필요하다. 본 논문에서는 주변 조명의 변화나 반사 등의 요인으로 인해 단일 임계값 이진화를 적용하기 어려워, 주변 화소 값을 함께 고려한 적응형 이진화 기법을 적용한다. 또한 누락되는 화소 부분을 보완하기 위해 컨벡스 헐 기법도 함께 적용한다. 그리고 분리된 영역에 적용할 학습 모델은 불량 특성이 비선형인 경우에 유리한 잔류 오차 기반의 심층학습 신경망 모델을 적용한다. 실험을 통해 제안하는 시스템이 오링의 불량 판별 자동화에 적용 가능하다는 것을 제시한다.

Keywords

Acknowledgement

This work was supported by Dong-eui University Grant.(202101970001)

References

  1. K. He, X. Zhang, S. Ren, and J. Sun, "Deep Residual Learning for Image Recognition," Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, pp. 770-778, 2016.
  2. H. J. Kang, "Efficient Fixed-Point Representation for ResNet-50 Convolutional Neural Network," Journal of the Korea Institute of Information and Communication Engineering, Korea, vol. 22, no. 1, pp. 1-8, May. 2018. https://doi.org/10.6109/JKIICE.2018.22.1.1
  3. M. L. Feng and Y. P. Tan, "Adaptive binarization method for document image analysis," 2004 IEEE International Conference on Multimedia and Expo (ICME) (IEEE Cat. No.04TH8763), Taipei, Taiwan, vol. 1, pp. 339-342, 2004.
  4. J. S. Kim, "Recognition Performance Improvement of QR and Color Codes Posted on Curved Surfaces," Journal of the Korea Institute of Information and Communication Engineering, vol. 23, no. 3, pp. 267-275, Mar. 2019. https://doi.org/10.6109/JKIICE.2019.23.3.267
  5. D. Avis, D. Bremner, and R. Seidel, "How good are convex hull algorithms?," Computational Geometry, vol. 7, no. 5-6, pp. 265-301, 1997. https://doi.org/10.1016/S0925-7721(96)00023-5
  6. H. S. Park and J. W. Lee, "Correct Linear Skyline Algorithm in High-Dimensional Space," Journal of the Korean Institute of Information Scientists and Engineers, vol. 45, no. 10, pp. 1089-1095, Oct. 2018.
  7. Muthukrishnan, Understanding Graham scan algorithm for finding the Convex hull of a set of Points [Internet]. Available: https://muthu.co/understanding-graham-scanalgorithm-for-finding-the-convex-hull-of-a-set-of-points/.
  8. M. K. Kim, "Feature Extraction on a Periocular Region and Person Authentication Using a ResNet Model," Journal of Korea Multimedia Society, vol. 22, no. 12, pp. 1347-1355, Dec. 2019.
  9. E. S. Seong and H. T. Kim, "Rubber O-ring defect detection using adaptive binarization, Convex Hull preprocessing, and convolutional neural network learning method," in Proceeding of the Korean Institute of Information and Communication Sciences Conference), pp. 623-625, 2021.