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Automated surface defect detection framework using machine vision and convolutional neural networks

Author

Listed:
  • Swarit Anand Singh

    (Indian Institute of Technology Jodhpur)

  • K. A. Desai

    (Indian Institute of Technology Jodhpur)

Abstract

Machine vision-based inspection technologies are gaining considerable importance for automated monitoring and quality control of manufactured products in recent years due to the advent of Industry 4.0. The involvement of advanced deep learning methods is a significant factor contributing to the advent of robust vision-based solutions for improving inspection accuracy at a significantly lower cost in manufacturing industries. The requirement of computational resources and large training datasets hinders the deployment of these solutions to manufacturing shop floors. The present research work develops an image-based framework considering pre-trained Convolutional Neural Network (CNN), ResNet-101 to detect surface defects with the minimum training datasets and computational requirements. The outcomes of the proposed framework are substantiated through a case study of detecting commonly observed surface defects during the centerless grinding of tapered rollers. The image datasets consisting of standard tapered rollers and three common defect classes are captured and enriched further with the help of the data augmentation technique. The present work employs ResNet-101 for feature extraction combined with and multi-class Support Vector Machine (SVM) as a classifier to detect defective images. The effects of the feature extraction layer (fc1000) and pooling layer (pool5) activation are explored to achieve the desired prediction abilities. The testing trials demonstrate that the proposed framework effectively performs image classification, achieving 100% precision for the ‘Good’ class components. The study showed that the proposed approach could overcome the requirements of large training datasets and higher computational power for deep learning models. The proposed system can be of significant importance for Micro, Small, and Medium Enterprises (MSMEs) and Small and Medium-sized Enterprises (SMEs) as an alternative to conventional labor-intensive manual inspection techniques.

Suggested Citation

  • Swarit Anand Singh & K. A. Desai, 2023. "Automated surface defect detection framework using machine vision and convolutional neural networks," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 1995-2011, April.
  • Handle: RePEc:spr:joinma:v:34:y:2023:i:4:d:10.1007_s10845-021-01878-w
    DOI: 10.1007/s10845-021-01878-w
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    References listed on IDEAS

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    1. Hui Lin & Bin Li & Xinggang Wang & Yufeng Shu & Shuanglong Niu, 2019. "Automated defect inspection of LED chip using deep convolutional neural network," Journal of Intelligent Manufacturing, Springer, vol. 30(6), pages 2525-2534, August.
    2. Ercan Oztemel & Samet Gursev, 2020. "Literature review of Industry 4.0 and related technologies," Journal of Intelligent Manufacturing, Springer, vol. 31(1), pages 127-182, January.
    3. Chung-Feng Jeffrey Kuo & Chun-Han Tsai & Wei-Ren Wang & Han-Cheng Wu, 2019. "Automatic marking point positioning of printed circuit boards based on template matching technique," Journal of Intelligent Manufacturing, Springer, vol. 30(2), pages 671-685, February.
    4. Sebastian Meister & Mahdieu A. M. Wermes & Jan Stüve & Roger M. Groves, 2021. "Review of image segmentation techniques for layup defect detection in the Automated Fiber Placement process," Journal of Intelligent Manufacturing, Springer, vol. 32(8), pages 2099-2119, December.
    5. Durga Prasad Penumuru & Sreekumar Muthuswamy & Premkumar Karumbu, 2020. "Identification and classification of materials using machine vision and machine learning in the context of industry 4.0," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1229-1241, June.
    6. Olatomiwa Badmos & Andreas Kopp & Timo Bernthaler & Gerhard Schneider, 2020. "Image-based defect detection in lithium-ion battery electrode using convolutional neural networks," Journal of Intelligent Manufacturing, Springer, vol. 31(4), pages 885-897, April.
    7. Domen Tabernik & Samo Šela & Jure Skvarč & Danijel Skočaj, 2020. "Segmentation-based deep-learning approach for surface-defect detection," Journal of Intelligent Manufacturing, Springer, vol. 31(3), pages 759-776, March.
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