IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v34y2023i3d10.1007_s10845-021-01864-2.html
   My bibliography  Save this article

Texture surface defect detection of plastic relays with an enhanced feature pyramid network

Author

Listed:
  • Feng Huang

    (Zhejiang University of Science and Technology
    China Jiliang University
    Zhejiang University)

  • Ben-wu Wang

    (China Jiliang University)

  • Qi-peng Li

    (Zhejiang University of Science and Technology)

  • Jun Zou

    (Zhejiang University)

Abstract

Deep learning has seen its promising applications in manufacturing processes. In this study, a deep network named Cascade Tri-DFPN based on the two-stage target detection algorithm is proposed for detecting the defects on the texture surface of plastic relays. The network adopts a derivative Resnet-101d as the backbone to obtain a loss-reduced feature extraction. Meanwhile, an enhanced feature pyramid module is put forward to enhance the feature representation of the network by adding a dense connection of feature layers through the self-attentive block. Moreover, the defect region proposals are optimized by introducing a cascade module to obtain high-quality defective proposal boxes. Experimental results on the augmented data set of relays’ surface defect reveal an average accuracy of 88.57% and an average recall rate of 94.58%, much higher than those of traditional RCNN or FPN detectors, demonstrating the remarkable improvement of the proposed network. Robustness of the method is also verified by performing tests with deteriorative image processing, which indicates an eligible defect detection under relatively complex scenarios such as image blurring. The proposed deep network could be used in surface defect detection of plastic relays and other potentially related industrial defect detection fields.

Suggested Citation

  • Feng Huang & Ben-wu Wang & Qi-peng Li & Jun Zou, 2023. "Texture surface defect detection of plastic relays with an enhanced feature pyramid network," Journal of Intelligent Manufacturing, Springer, vol. 34(3), pages 1409-1425, March.
    • Handle: RePEc:spr:joinma:v:34:y:2023:i:3:d:10.1007_s10845-021-01864-2
    DOI: 10.1007/s10845-021-01864-2
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-021-01864-2
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10845-021-01864-2?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. Cheng Hao Jin & Hyun-Jin Kim & Yongjun Piao & Meijing Li & Minghao Piao, 2020. "Wafer map defect pattern classification based on convolutional neural network features and error-correcting output codes," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 1861-1875, December.
    3. Lu Yang & Hongquan Jiang, 2021. "Weld defect classification in radiographic images using unified deep neural network with multi-level features," Journal of Intelligent Manufacturing, Springer, vol. 32(2), pages 459-469, February.
    4. Haiyong Chen & Yue Pang & Qidi Hu & Kun Liu, 2020. "Solar cell surface defect inspection based on multispectral convolutional neural network," Journal of Intelligent Manufacturing, Springer, vol. 31(2), pages 453-468, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chia-Yu Hsu & Ju-Chien Chien, 2022. "Ensemble convolutional neural networks with weighted majority for wafer bin map pattern classification," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 831-844, March.
    2. Chengjun Xu & Guobin Zhu, 2021. "Intelligent manufacturing Lie Group Machine Learning: real-time and efficient inspection system based on fog computing," Journal of Intelligent Manufacturing, Springer, vol. 32(1), pages 237-249, January.
    3. Aidong Chen & Xiang Li & Hongyuan Jing & Chen Hong & Minghai Li, 2023. "Anomaly Detection Algorithm for Photovoltaic Cells Based on Lightweight Multi-Channel Spatial Attention Mechanism," Energies, MDPI, vol. 16(4), pages 1-15, February.
    4. Nhat-To Huynh & Duong-Dong Ho & Hong-Nguyen Nguyen, 2023. "An Approach for Designing an Optimal CNN Model Based on Auto-Tuning GA with 2D Chromosome for Defect Detection and Classification," Sustainability, MDPI, vol. 15(6), pages 1-14, March.
    5. Chiwu Bu & Tao Liu & Tao Wang & Hai Zhang & Stefano Sfarra, 2023. "A CNN-Architecture-Based Photovoltaic Cell Fault Classification Method Using Thermographic Images," Energies, MDPI, vol. 16(9), pages 1-13, April.
    6. Bikash Koli Dey & Hyesung Seok, 2024. "Intelligent inventory management with autonomation and service strategy," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 307-330, January.
    7. Cheng Hao Jin & Hyun-Jin Kim & Yongjun Piao & Meijing Li & Minghao Piao, 2020. "Wafer map defect pattern classification based on convolutional neural network features and error-correcting output codes," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 1861-1875, December.
    8. Shuo Meng & Ruru Pan & Weidong Gao & Jian Zhou & Jingan Wang & Wentao He, 2021. "A multi-task and multi-scale convolutional neural network for automatic recognition of woven fabric pattern," Journal of Intelligent Manufacturing, Springer, vol. 32(4), pages 1147-1161, April.
    9. Meng Xiao & Bo Yang & Shilong Wang & Yongsheng Chang & Song Li & Gang Yi, 2023. "Research on recognition methods of spot-welding surface appearances based on transfer learning and a lightweight high-precision convolutional neural network," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2153-2170, June.
    10. Diyi Zhou & Shihua Gong & Ziyue Wang & Delong Li & Huaiqing Lu, 2021. "Error analysis based on error transfer theory and compensation strategy for LED chip visual localization systems," Journal of Intelligent Manufacturing, Springer, vol. 32(5), pages 1345-1359, June.
    11. Tae San Kim & Jong Wook Lee & Won Kyung Lee & So Young Sohn, 2022. "Novel method for detection of mixed-type defect patterns in wafer maps based on a single shot detector algorithm," Journal of Intelligent Manufacturing, Springer, vol. 33(6), pages 1715-1724, August.
    12. Yuwei Mao & Hui Lin & Christina Xuan Yu & Roger Frye & Darren Beckett & Kevin Anderson & Lars Jacquemetton & Fred Carter & Zhangyuan Gao & Wei-keng Liao & Alok N. Choudhary & Kornel Ehmann & Ankit Agr, 2023. "A deep learning framework for layer-wise porosity prediction in metal powder bed fusion using thermal signatures," Journal of Intelligent Manufacturing, Springer, vol. 34(1), pages 315-329, January.
    13. 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.
    14. Zhuxi Ma & Yibo Li & Minghui Huang & Qianbin Huang & Jie Cheng & Si Tang, 2023. "Automated real-time detection of surface defects in manufacturing processes of aluminum alloy strip using a lightweight network architecture," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2431-2447, June.
    15. Minghao Piao & Cheng Hao Jin, 2023. "CNN and ensemble learning based wafer map failure pattern recognition based on local property based features," Journal of Intelligent Manufacturing, Springer, vol. 34(8), pages 3599-3621, December.
    16. Minyoung Lee & Joohyoung Jeon & Hongchul Lee, 2022. "Explainable AI for domain experts: a post Hoc analysis of deep learning for defect classification of TFT–LCD panels," Journal of Intelligent Manufacturing, Springer, vol. 33(6), pages 1747-1759, August.
    17. Feiyang Li & Nian Cai & Xueliang Deng & Jiahao Li & Jianfa Lin & Han Wang, 2022. "Serial number inspection for ceramic membranes via an end-to-end photometric-induced convolutional neural network framework," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1373-1392, June.
    18. Yunhan Kim & Taekyum Kim & Byeng D. Youn & Sung-Hoon Ahn, 2022. "Machining quality monitoring (MQM) in laser-assisted micro-milling of glass using cutting force signals: an image-based deep transfer learning," Journal of Intelligent Manufacturing, Springer, vol. 33(6), pages 1813-1828, August.
    19. Ruizhen Liu & Zhiyi Sun & Anhong Wang & Kai Yang & Yin Wang & Qianlai Sun, 2020. "Real-time defect detection network for polarizer based on deep learning," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 1813-1823, December.
    20. Tongwha Kim & Kamran Behdinan, 2023. "Advances in machine learning and deep learning applications towards wafer map defect recognition and classification: a review," Journal of Intelligent Manufacturing, Springer, vol. 34(8), pages 3215-3247, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:joinma:v:34:y:2023:i:3:d:10.1007_s10845-021-01864-2. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.