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Year 2024, Volume: 7 Issue: 1, 36 - 45, 30.04.2024

Emel Soylu , İbrahim Kaya

https://doi.org/10.35377/saucis...1391636

Abstract

References

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  • [11] E. Soylu, “A Deep Transfer Learning-Based Comparative Study for Detection of Malaria Disease,” Sak. Univ. J. Comput. Inf. Sci., vol. 5, no. 3, pp. 427–447, 2022.
  • [12] S. S. Yadav and S. M. Jadhav, “Deep convolutional neural network based medical image classification for disease diagnosis,” J. Big data, vol. 6, no. 1, pp. 1–18, 2019.
  • [13] M. A. Chandra and S. S. Bedi, “Survey on SVM and their application in image classification,” Int. J. Inf. Technol., vol. 13, pp. 1–11, 2021.
  • [14] C.-C. Yang et al., “Application of decision tree technology for image classification using remote sensing data,” Agric. Syst., vol. 76, no. 3, pp. 1101–1117, 2003.
  • [15] M. Sheykhmousa, M. Mahdianpari, H. Ghanbari, F. Mohammadimanesh, P. Ghamisi, and S. Homayouni, “Support vector machine versus random forest for remote sensing image classification: A meta-analysis and systematic review,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 13, pp. 6308–6325, 2020.
  • [16] J. Kim, B.-S. Kim, and S. Savarese, “Comparing image classification methods: K-nearest-neighbor and support-vector-machines,” in Proceedings of the 6th WSEAS international conference on Computer Engineering and Applications, and Proceedings of the 2012 American conference on Applied Mathematics, 2012, pp. 133–138.
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  • [22] M. A. Mallaiyan Sathiaseelan, O. P. Paradis, S. Taheri, and N. Asadizanjani, “Why is deep learning challenging for printed circuit board (pcb) component recognition and how can we address it?,” Cryptography, vol. 5, no. 1, p. 9, 2021.
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  • [25] Y. Cheng, A. Wang, and L. Wu, “A Classification Method for Electronic Components Based on Siamese Network,” Sensors, vol. 22, no. 17, 2022.
  • [26] D. Lefkaditis and G. Tsirigotis, “Intelligent optical classification system for electronic components,” Elektron. ir Elektrotechnika, vol. 2, no. 2, pp. 10–14, 2010.
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  • [29] H. Alhichri, A. S. Alswayed, Y. Bazi, N. Ammour, and N. A. Alajlan, “Classification of remote sensing images using EfficientNet-B3 CNN model with attention,” IEEE access, vol. 9, pp. 14078–14094, 2021.
  • [30] Y. Chen, H. Liang, and S. Pang, “Study on small samples active sonar target recognition based on deep learning,” J. Mar. Sci. Eng., vol. 10, no. 8, p. 1144, 2022.
  • [31] X. Chen et al., “Application of EfficientNet-B0 and GRU-based deep learning on classifying the colposcopy diagnosis of precancerous cervical lesions,” Cancer Med., vol. 12, no. 7, pp. 8690–8699, 2023.
  • [32] C. Wang et al., “Pulmonary image classification based on inception-v3 transfer learning model,” IEEE Access, vol. 7, pp. 146533–146541, 2019.
  • [33] Y. Nan, J. Ju, Q. Hua, H. Zhang, and B. Wang, “A-MobileNet: An approach of facial expression recognition,” Alexandria Eng. J., vol. 61, no. 6, pp. 4435–4444, 2022.
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  • [35] “Resistor Dataset.” [Online]. Available: https://www.kaggle.com/datasets/eralpozcan/resistor-dataset.
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  • [37] “Transistor BC BD.” [Online]. Available: https://www.kaggle.com/datasets/josevitormichelin/transistor-bc-bd/code.
  • [38] “Keras Applications.” [Online]. Available: https://keras.io/api/applications.
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  • [40] M. Tan and Q. Le, “Efficientnet: Rethinking model scaling for convolutional neural networks,” in International conference on machine learning, 2019, pp. 6105–6114.

Classification of Electronics Components using Deep Learning

Year 2024, Volume: 7 Issue: 1, 36 - 45, 30.04.2024

Emel Soylu , İbrahim Kaya

https://doi.org/10.35377/saucis...1391636

Abstract

In this study, we present an advanced electronic component classification system with an exceptional classification accuracy exceeding 99% using state-of-the-art deep learning architectures. We employed EfficientNetV2B3, EfficientNetV2S, EfficientNetB0, InceptionV3, MobileNet, and Vision Transformer (ViT) models for the classification task. The system demonstrates the remarkable potential of these deep learning models in handling complex visual recognition tasks, specifically in the domain of electronic components. Our dataset comprises a diverse set of electronic components, and we meticulously curated and labeled it to ensure high-quality training data. We conducted extensive experiments to fine-tune and optimize the models for the given task, leveraging data augmentation techniques and transfer learning. The high classification accuracy achieved by our system indicates its readiness for real-world deployment, marking a significant step towards advancing automation and efficiency in the electronics industry.

Keywords

Electronic component classification Deep learning Transfer learning

References

  • [1] A. A. Almubarak, “The effects of heat on electronic components,” Int. J. Eng. Res. Appl, vol. 7, no. 5, pp. 52–57, 2017.
  • [2] M. Pecht, P. Lall, G. Ballou, C. Sankaran, and N. Angelopoulos, “Passive components,” in Circuits, Signals, and Speech and Image Processing, CRC Press, 2018, p. 1.
  • [3] Z. Fu, J. Wang, A. Bretas, Y. Ou, and G. Zhou, “Measurement method for resistive current components of metal oxide surge arrester in service,” IEEE Trans. Power Deliv., vol. 33, no. 5, pp. 2246–2253, 2017.
  • [4] P. Hauptmann, N. Hoppe, and A. Püttmer, “Application of ultrasonic sensors in the process industry,” Meas. Sci. Technol., vol. 13, no. 8, p. R73, 2002.
  • [5] X. D. Zhang, L. Y. Kang, and W. F. Diao, “The principle of the potentiometer and its applications in the vehicle steering,” in IEEE International Conference on Vehicular Electronics and Safety, 2005., 2005, pp. 20–24.
  • [6] Y. Yang, X. Tong, L.-T. Yang, P.-F. Guo, L. Fan, and Y.-C. Yeo, “Tunneling field-effect transistor: capacitance components and modeling,” IEEE Electron Device Lett., vol. 31, no. 7, pp. 752–754, 2010.
  • [7] A. De Donatis, “The Button Component,” Adv. ActionScript Components Mastering Flash Compon. Archit., pp. 275–293, 2006.
  • [8] Q. J. Harmer, P. M. Weaver, and K. M. Wallace, “Design-led component selection,” Comput. Des., vol. 30, no. 5, pp. 391–405, 1998.
  • [9] B. Eisenberg, N. Gold, Z. Song, and H. Huang, “What current flows through a resistor?,” arXiv Prepr. arXiv1805.04814, 2018.
  • [10] W. J. Sarjeant, I. W. Clelland, and R. A. Price, “Capacitive components for power electronics,” Proc. IEEE, vol. 89, no. 6, pp. 846–855, 2001.
  • [11] E. Soylu, “A Deep Transfer Learning-Based Comparative Study for Detection of Malaria Disease,” Sak. Univ. J. Comput. Inf. Sci., vol. 5, no. 3, pp. 427–447, 2022.
  • [12] S. S. Yadav and S. M. Jadhav, “Deep convolutional neural network based medical image classification for disease diagnosis,” J. Big data, vol. 6, no. 1, pp. 1–18, 2019.
  • [13] M. A. Chandra and S. S. Bedi, “Survey on SVM and their application in image classification,” Int. J. Inf. Technol., vol. 13, pp. 1–11, 2021.
  • [14] C.-C. Yang et al., “Application of decision tree technology for image classification using remote sensing data,” Agric. Syst., vol. 76, no. 3, pp. 1101–1117, 2003.
  • [15] M. Sheykhmousa, M. Mahdianpari, H. Ghanbari, F. Mohammadimanesh, P. Ghamisi, and S. Homayouni, “Support vector machine versus random forest for remote sensing image classification: A meta-analysis and systematic review,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 13, pp. 6308–6325, 2020.
  • [16] J. Kim, B.-S. Kim, and S. Savarese, “Comparing image classification methods: K-nearest-neighbor and support-vector-machines,” in Proceedings of the 6th WSEAS international conference on Computer Engineering and Applications, and Proceedings of the 2012 American conference on Applied Mathematics, 2012, pp. 133–138.
  • [17] J. Schmidhuber, “Deep learning,” Scholarpedia, vol. 10, no. 11, p. 32832, 2015.
  • [18] Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, p. 436, May 2015.
  • [19] N. Rusk, “Deep learning,” Nat. Methods, vol. 13, no. 1, p. 35, 2016.
  • [20] L. C. Yan, B. Yoshua, and H. Geoffrey, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015.
  • [21] J. Li, W. Li, Y. Chen, and J. Gu, “A PCB Electronic Components Detection Network Design Based on Effective Receptive Field Size and Anchor Size Matching,” Comput. Intell. Neurosci., vol. 2021, p. 6682710, 2021.
  • [22] M. A. Mallaiyan Sathiaseelan, O. P. Paradis, S. Taheri, and N. Asadizanjani, “Why is deep learning challenging for printed circuit board (pcb) component recognition and how can we address it?,” Cryptography, vol. 5, no. 1, p. 9, 2021.
  • [23] M. A. Reza, Z. Chen, and D. J. Crandall, “Deep neural network--based detection and verification of microelectronic images,” J. Hardw. Syst. Secur., vol. 4, no. 1, pp. 44–54, 2020.
  • [24] A. Bhattacharya, S. Roy, N. Sarkar, S. Malakar, and R. Sarkar, “Circuit Component Detection in Offline Handdrawn Electrical/Electronic Circuit Diagram,” in 2020 IEEE Calcutta Conference (CALCON), 2020, pp. 80–84.
  • [25] Y. Cheng, A. Wang, and L. Wu, “A Classification Method for Electronic Components Based on Siamese Network,” Sensors, vol. 22, no. 17, 2022.
  • [26] D. Lefkaditis and G. Tsirigotis, “Intelligent optical classification system for electronic components,” Elektron. ir Elektrotechnika, vol. 2, no. 2, pp. 10–14, 2010.
  • [27] Y. J. Wang et al., “An Artificial Neural Network to Support Package Classification for SMT Components,” 2018 3rd Int. Conf. Comput. Commun. Syst. ICCCS 2018, pp. 173–177, 2018.
  • [28] J. Huang and Y. Lu, “A Method for Identifying and Classifying Resistors and Capacitors Based on YOLO Network,” in 2019 IEEE 4th International Conference on Signal and Image Processing (ICSIP), 2019, pp. 1–5.
  • [29] H. Alhichri, A. S. Alswayed, Y. Bazi, N. Ammour, and N. A. Alajlan, “Classification of remote sensing images using EfficientNet-B3 CNN model with attention,” IEEE access, vol. 9, pp. 14078–14094, 2021.
  • [30] Y. Chen, H. Liang, and S. Pang, “Study on small samples active sonar target recognition based on deep learning,” J. Mar. Sci. Eng., vol. 10, no. 8, p. 1144, 2022.
  • [31] X. Chen et al., “Application of EfficientNet-B0 and GRU-based deep learning on classifying the colposcopy diagnosis of precancerous cervical lesions,” Cancer Med., vol. 12, no. 7, pp. 8690–8699, 2023.
  • [32] C. Wang et al., “Pulmonary image classification based on inception-v3 transfer learning model,” IEEE Access, vol. 7, pp. 146533–146541, 2019.
  • [33] Y. Nan, J. Ju, Q. Hua, H. Zhang, and B. Wang, “A-MobileNet: An approach of facial expression recognition,” Alexandria Eng. J., vol. 61, no. 6, pp. 4435–4444, 2022.
  • [34] A. Steiner, A. Kolesnikov, X. Zhai, R. Wightman, J. Uszkoreit, and L. Beyer, “How to train your vit? data, augmentation, and regularization in vision transformers,” arXiv Prepr. arXiv2106.10270, 2021.
  • [35] “Resistor Dataset.” [Online]. Available: https://www.kaggle.com/datasets/eralpozcan/resistor-dataset.
  • [36] “Electronic Components and devices.” [Online]. Available: https://www.kaggle.com/datasets/aryaminus/electronic-components/code.
  • [37] “Transistor BC BD.” [Online]. Available: https://www.kaggle.com/datasets/josevitormichelin/transistor-bc-bd/code.
  • [38] “Keras Applications.” [Online]. Available: https://keras.io/api/applications.
  • [39] C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the inception architecture for computer vision,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 2818–2826.
  • [40] M. Tan and Q. Le, “Efficientnet: Rethinking model scaling for convolutional neural networks,” in International conference on machine learning, 2019, pp. 6105–6114.
There are 40 citations in total.

Details

Primary Language English
Subjects Computer Software
Journal Section Articles
Authors

Emel Soylu 0000-0003-2774-9778

İbrahim Kaya 0000-0002-8334-6669

Early Pub Date April 27, 2024
Publication Date April 30, 2024
Submission Date November 16, 2023
Acceptance Date January 30, 2024
Published in Issue Year 2024Volume: 7 Issue: 1

Cite

IEEE E. Soylu and İ. Kaya, “Classification of Electronics Components using Deep Learning”, SAUCIS, vol. 7, no. 1, pp. 36–45, 2024, doi: 10.35377/saucis...1391636.
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