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Edge Intelligence with Light Weight CNN Model for Surface Defect Detection in Manufacturing Industry


Affiliations
1 Institute of Aeronautical Engineering College, Hyderabad 500 043, Telangana, India
2 Prasad V. Potluri Siddhartha Institute of Technology, Vijayawada 520 007, Andhra Pradesh, India
3 Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada 520 007, Andhra Pradesh, India
4 Sree Vidyanikethan Engineering College, Tirupati 517 102, Andhra Pradesh, India
 

Surface defect identification is essential for maintaining and improving the quality of industrial products. However, numerous environmental factors, including reflection, radiance, light, and material, affect the defect detection process, considerably increasing the difficulty of detecting surface defects. Deep Learning, a part of Artificial intelligence, can detect surface defects in the industrial sector. However, conventional deep learning techniques are heavy in terms of expensive GPU requirements to support massive computations during the defect detection process.CondenseNetV2, a Lightweight CNN-based model, which performs well on microscopic defect inspection, and can be operated on low-frequency edge devices, was proposed in this research. It provides sufficient feature extractions with little computational overhead by reusing a set of the existing Sparse Feature Reactivation module. The training data are subjected to data augmentation techniques, and the hyper-parameters of the proposed model are fine-tuned with transfer learning. The model was tested extensively with two real datasets while running on an edge device (NVIDIA Jetson Xavier Nx SOM). The experiment results confirm that the projected model can efficiently detect the faults in the real-world environment while reliably and robustly diagnosing them.

Keywords

CondenseNet, Convolutional Neural Networks, Deep Learning, Edge Device, Industrial Products.
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  • Kergroach S, Industry 4.0: New challenges and opportunities for the labourmarket, Форсайт, 11(4) (2017) 6–8.

  • Rana A K & Sharma S, Industry 4.0 manufacturing based on IoT, cloud computing, and big data: Manufacturing purpose scenario, Adv Commun Comput Technol, 668 (2021) 1109–1119.

  • Rehman M H U, Yaqoob I, Salah K, Imran M, Jayaraman P P & Perera C, The role of big data analytics in industrial Internet of Things, Future Gener Comput Syst, 99 (2019) 247–259.

  • Nord J H, Koohang A & Paliszkiewicz J, The Internet of Things: Review and theoretical framework, Expert Syst Appl, 133 (2019) 97–108.

  • Lee, J, Davari H, Singh J & Pandhare V, Industrial Artificial Intelligence for industry 4.0-based manufacturing systems, Manuf Lett, 18 (2018) 20–23.

  • Janakiramaiah B, Kalyani G, Prasad L V, Karuna A & Krishna M, Intelligent system for leaf disease detection using capsule networks for horticulture, J Intell Fuzzy Syst, 41(6) (2021) 6697–6713.

  • Hashemi-Petroodi S E, Thevenin S, Kovalev S and Dolgui A, Operations management issues in design and control of hybrid human-robot collaborative manufacturing systems: a survey, Annu Rev Control, 49 (2020) 264–276.

  • Singh H, Big data, industry 4.0 and cyber-physical systems integration: A smart industry context, Mater Today Proc, 46 (2021) 157–162.

  • De Vitis G A, Foglia P & Prete C A, Row‐level algorithm to improve real‐time performance of glass tube defect detection in the production phase, IET Image Process, 14(12) (2020) 2911–2921.

  • Jain S, Seth G, Paruthi A, Soni U & Kumar G, Synthetic data augmentation for surface defect detection and classification using deep learning, J Intell Manuf, (2020) 1–14.

  • Zhuxi M A, Li Y, Huang M, Huang Q, Cheng J & Tang S, A light weight detector based on attention mechanism for aluminum strip surface defect detection, Comput Ind, 136 (2022) 103585.

  • Janakiramaiah B, Kalyani G, Karuna A, Prasad L V & Krishna M, Military object detection in defense using multi-level capsule networks, Soft Comput, 27 (2023) 1045–1059.

  • Kalyani G, Janakiramaiah B, Karuna A & Prasad L V, Diabetic retinopathy detection and classification using capsule networks, Complex Intell Syst, (2021) 1–14, https://doi.org/10.1007/s40747-021-00318-9.

  • Subramanian M, Lv N P & Ve S, Hyperparameter optimization for transfer learning of VGG16 for disease identification in corn leaves using Bayesian optimization, Big Data, 10(3) (2022) 215–229.

  • Wan G, Fang H, Wang D, Yan J & Xie B, Ceramic tile surface defect detection based on deep learning, Ceram Int, 48 (8) (2022) 11085–11093.

  • Pan Z, Yang J, Wang X.E, Wang F, Azim I & Wang C, Image-based surface scratch detection on architectural glass panels using deep learning approach, Construction and Building Materials, 282 (2021) 122717.

  • Sahoo S K, Mahesh Sharma M & Choudhury B B, A dynamic bottle inspection structure, in Computational Intelligence in Data Mining, Advances in Intelligent Systems and Computing, vol 711, edited by H Behera, J Nayak, B Naik, A Abraham (Springer, Singapore) 2019, 873–884, https://doi.org/10.1007/978-981-10-8055-5_77.

  • Ren R, Hung T & Tan K C A generic deep-learning-based approach for automated surface inspection, IEEE Trans Cybernet, 48(3) (2017) 929–940.

  • Ye R, Chang M, Pan C S, Chiang C A & Gabayno J L, High-resolution optical inspection system for fast detection and classification of surface defects, Int J Optomechatronics, 12(1) (2018) 1–10.

  • Liu R, Huang M, and Cao P, An end-to-end steel strip surface defects detection framework: considering complex background interference, In 33rd Chinese Control and Decis Conf (CCDC) China, 2021, 317–322.

  • Jiang J, Cao P, Lu Z, Lou W & Yang Y, Surface defect detection for mobile phone back glass based on symmetric convolutional neural network deep learning, Appl Sci, 10(10) (2020) 3621.

  • Fu J, Zhu X & Li Y, Recognition of Surface Defects on Steel Sheet using Transfer Learning (Xian Jiaotong University) 2020, 25–34.

  • Huang G, Liu S, van der Maaten L & Weinberger K Q, Condensenet: An efficient densenet using learned group convolutions, In Proc IEEE Conf Comput Vis Pattern Recognit (Salt Lake City, UT, USA) 2018, 2752–2761.

  • Schlagenhauf T & Landwehr M, Industrial machine tool component surface defect dataset, Data in Brief, 39 (2021) 107643.

  • Schlagenhauf T, Ball Screw Drive Surface Defect Dataset for Classification (Karlsruher Institut für Technologie) 2021.


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  • Edge Intelligence with Light Weight CNN Model for Surface Defect Detection in Manufacturing Industry

Abstract Views: 115  |  PDF Views: 80

Authors

Shobha Rani D
Institute of Aeronautical Engineering College, Hyderabad 500 043, Telangana, India
Lakshmi Ramani Burra
Prasad V. Potluri Siddhartha Institute of Technology, Vijayawada 520 007, Andhra Pradesh, India
Kalyani G
Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada 520 007, Andhra Pradesh, India
Narendra Kumar Rao B
Sree Vidyanikethan Engineering College, Tirupati 517 102, Andhra Pradesh, India

Abstract


Surface defect identification is essential for maintaining and improving the quality of industrial products. However, numerous environmental factors, including reflection, radiance, light, and material, affect the defect detection process, considerably increasing the difficulty of detecting surface defects. Deep Learning, a part of Artificial intelligence, can detect surface defects in the industrial sector. However, conventional deep learning techniques are heavy in terms of expensive GPU requirements to support massive computations during the defect detection process.CondenseNetV2, a Lightweight CNN-based model, which performs well on microscopic defect inspection, and can be operated on low-frequency edge devices, was proposed in this research. It provides sufficient feature extractions with little computational overhead by reusing a set of the existing Sparse Feature Reactivation module. The training data are subjected to data augmentation techniques, and the hyper-parameters of the proposed model are fine-tuned with transfer learning. The model was tested extensively with two real datasets while running on an edge device (NVIDIA Jetson Xavier Nx SOM). The experiment results confirm that the projected model can efficiently detect the faults in the real-world environment while reliably and robustly diagnosing them.

Keywords


CondenseNet, Convolutional Neural Networks, Deep Learning, Edge Device, Industrial Products.

References