Keywords:
deep learning
feature pyramid network
PCB
defect detection
image processing
smart manufacturing

  • Abstract

    The ever-expanding market for electronic devices has significantly heightened the demand for high-quality printed circuit boards (PCBs). Even minor defects in PCBs can pose substantial safety risks for end-users. This article provides a comprehensive review on deep learning-based approaches for PCB defect detection. Our exploration covers various critical aspects, including the classification of PCB defects, automated vision inspection (AVI) techniques, object detection methodologies, and the widespread adoption of deep learning models. Specifically, we focus on the state-of-the-art approach known as region-based Fully Convolutional Network with Feature Pyramid Networks (FPN-RFCN). Additionally, we discuss effective data augmentation techniques and commonly used evaluation metrics in this domain. This review provides valuable insights for researchers, practitioners, and industry professionals engaged in PCB quality assurance.

  • References

    1. Arif, Z. H., Mahmoud, M. A., Abdulkareem, K. H., Mohammed, M. A., Al-Mhiqani, M. N., Mutlag, A. A., & Damaševičius, R. (2022). Comprehensive Review of Machine Learning (ML) in Image Defogging: Taxonomy of Concepts, Scenes, Feature Extraction, and Classification techniques. IET Image Processing, 16(2), 289–310. https://doi.org/10.1049/IPR2.12365
    2. Awang, N., Fauadi, M. H. F. M., & Rosli, N. S. (2015). Image Processing of Product Surface Defect Using Scilab. Applied Mechanics and Materials vols. 789–790, pp. 1223–1226. https://doi.org/10.4028/www.scientific.net/amm.789-790.1223
    3. Bahrebar, S., Homayoun, S., & Ambat, R. (2022). Using machine learning algorithms to predict failure on the PCB surface under corrosive conditions. Corrosion Science, 206, 110500. https://doi.org/10.1016/J.CORSCI.2022.110500
    4. Boateng, E. Y., Otoo, J., Abaye, D. A., Boateng, E. Y., Otoo, J., & Abaye, D. A. (2020). Basic Tenets of Classification Algorithms K-Nearest-Neighbor, Support Vector Machine, Random Forest and Neural Network: A Review. Journal of Data Analysis and Information Processing, 8(4), 341–357. https://doi.org/10.4236/JDAIP.2020.84020
    5. Chen, I. C., Hwang, R. C., & Huang, H. C. (2023). PCB Defect Detection Based on Deep Learning Algorithm. Processes, Vol. 11, Page 775, 11(3), 775. https://doi.org/10.3390/PR11030775
    6. Chen, L., Li, S., Bai, Q., Yang, J., Jiang, S., & Miao, Y. (2021). Review of Image Classification Algorithms Based on Convolutional Neural Networks. Remote Sensing, Vol. 13, Page 4712, 13(22), 4712. https://doi.org/10.3390/RS13224712
    7. Dai, J., Li, Y., He, K., & Sun, J. (2016). R-FCN: Object Detection via Region-based Fully Convolutional Networks. Advances in Neural Information Processing Systems, 379–387. https://arxiv.org/abs/1605.06409v2
    8. Das, K., & Zhang, Q. (2020). Convolutional Recurrent Residual U-Net Embedded with Attention Mechanism and Focal Tversky Loss Function for Cancerous Nuclei Detection. Queen Mary University of London, United Kingdom. https://arxiv.org/abs/2010.04416v1
    9. Ding, R., Dai, L., Li, G., & Liu, H. (2019). TDD-net: a tiny defect detection network for printed circuit boards. CAAI Transactions on Intelligence Technology, 4(2), 110–116. https://doi.org/10.1049/TRIT.2019.0019
    10. Dong, Y., Wang, Z., & Guo, W. (2022). Overview of edge detection algorithms based on mathematical morphology. In Proceedings of Electronic and Automation Control Conference (IAEAC), 2022-October, 1321–1326. https://doi.org/10.1109/IAEAC54830.2022.9930043
    11. Du, Y., & Wang, Q. (2021). Multi-Angle Face Detection Based on Improved RFCN Algorithm Using Multi-Scale Training. In Proceedings of 2021 IEEE 6th International Conference on Intelligent Computing and Signal Processing, ICSP 2021, 319–322. https://doi.org/10.1109/ICSP51882.2021.9408676
    12. Fung, K. C., Xue, K. W., Lai, C. M., Lin, K. H., & Lam, K. M. (2024). Improving PCB defect detection using selective feature attention and pixel shuffle pyramid. Results in Engineering, 21, 101992. https://doi.org/10.1016/J.RINENG.2024.101992
    13. Ge, C., Wang, J., Wang, J., Qi, Q., Sun, H., & Liao, J. (2020). Towards automatic visual inspection: A weakly supervised learning method for industrial applicable object detection. Computers in Industry, 121, 103232. https://doi.org/10.1016/J.COMPIND.2020.103232
    14. Georgiev, A. G., Hristov, G., Plamen Zahariev, A., & Diyana Kinaneva, A. (2023). Exploring Object Detection Algorithms: A Comprehensive Overview and Comparative Study 15. Proceedings of University of Ruse-2023, 62.
    15. Ghaffari, S., Soleimani, P., Li, K. F., & Capson, D. W. (2020). Analysis and Comparison of FPGA-Based Histogram of Oriented Gradients Implementations. IEEE Access, 8, 79920–79934. https://doi.org/10.1109/ACCESS.2020.2989267
    16. Hu, B., & Wang, J. (2020a). Detection of PCB Surface Defects with Improved Faster-RCNN and Feature Pyramid Network. IEEE Access, 8, 108335–108345. https://doi.org/10.1109/ACCESS.2020.3001349
    17. Iman, M., Arabnia, H. R., & Rasheed, K. (2023). A Review of Deep Transfer Learning and Recent Advancements. Technologies, Vol. 11, Page 40, 11(2), 40. https://doi.org/10.3390/TECHNOLOGIES11020040
    18. Lan, Z., Hong, Y., & Li, Y. (2021). An improved YOLOv3 method for PCB surface defect detection. In Proceedings of 2021 IEEE International Conference on Power Electronics, Computer Applications, ICPECA 2021, 1009–1015. https://doi.org/10.1109/ICPECA51329.2021.9362675
    19. Li, C. J., Qu, Z., Wang, S. Y., Bao, K. H., & Wang, S. Y. (2022). A Method of Defect Detection for Focal Hard Samples PCB Based on Extended FPN Model. IEEE Transactions on Components, Packaging and Manufacturing Technology, 12(2), 217–227. https://doi.org/10.1109/TCPMT.2021.3136823
    20. Li, Y., Miao, N., Ma, L., Shuang, F., & Huang, X. (2023). Transformer for object detection: Review and benchmark. Engineering Applications of Artificial Intelligence, 126, 107021. https://doi.org/10.1016/J.ENGAPPAI.2023.107021
    21. Li, Z., Liu, F., Yang, W., Peng, S., & Zhou, J. (2022). A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects. IEEE Transactions on Neural Networks and Learning Systems, 33(12), 6999–7019. https://doi.org/10.1109/TNNLS.2021.3084827
    22. Lin, W., Wu, P., & Xiao, X. (2021). Boundary Focal Loss for Class Imbalanced Learning. In Proceedings of 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics. https://doi.org/10.1109/CISP-BMEI53629.2021.9624339
    23. Ling, Q., Isa, N. A. M., & Asaari, M. S. M. (2023). Precise Detection for Dense PCB Components Based on Modified YOLOv8. IEEE Access, 11, 116545–116560. https://doi.org/10.1109/ACCESS.2023.3325885
    24. Linton, P., Morgan, M. J., Read, J. C. A., Vishwanath, D., Creem-Regehr, S. H., & Domini, F. (2023). New Approaches to 3D Vision. Philosophical Transactions of the Royal Society B, 378(1869). https://doi.org/10.1098/RSTB.2021.0443
    25. Liu, X., Hu, J., Wang, H., Zhang, Z., Lu, X., Sheng, C., Song, S., & Nie, J. (2022). Gaussian-IoU loss: Better learning for bounding box regression on PCB component detection. Expert Systems with Applications, 190, 116178. https://doi.org/10.1016/J.ESWA.2021.116178
    26. Lou, L., Lu, K., & Xue, J. (2023). Multi-Scale Vision Transformer for Defect Object Detection. Procedia Computer Science, 222, 397–406. https://doi.org/10.1016/J.PROCS.2023.08.178
    27. LU, H., & MAZUMDER, R. (2020). Randomized Gradient Boosting Machine. SIAM Journal on Optimization, 30(4), 2780–2808. https://doi.org/10.1137/18M1223277
    28. Mercier, J. P., Garon, M., Giguere, P., & Lalonde, J. F. (2021). Deep template-based object instance detection. In Proceedings of 2021 IEEE Winter Conference on Applications of Computer Vision, WACV 2021, 1506–1515. https://doi.org/10.1109/WACV48630.2021.00155
    29. Mohan, L., Pant, J., Suyal, P., & Kumar, A. (2020). Support Vector Machine Accuracy Improvement with Classification. In Proceedings of 12th International Conference on Computational Intelligence and Communication Networks, 477–481. https://doi.org/10.1109/CICN49253.2020.9242572
    30. O’Mahony, N., Campbell, S., Carvalho, A., Harapanahalli, S., Hernandez, G. V., Krpalkova, L., Riordan, D., & Walsh, J. (2020). Deep Learning vs. Traditional Computer Vision. Advances in Intelligent Systems and Computing, 943, 128–144. https://doi.org/10.1007/978-3-030-17795-9_10
    31. Pang, G., Shen, C., Cao, L., & Hengel, A. Van Den. (2021). Deep Learning for Anomaly Detection. ACM Computing Surveys (CSUR), 54(2). https://doi.org/10.1145/3439950
    32. Patel, P., & Thakkar, A. (2020). The upsurge of deep learning for computer vision applications. International Journal of Electrical and Computer Engineering (IJECE), 10(1), 538–548. https://doi.org/10.11591/ijece.v10i1.pp538-548
    33. Piliposyan, G., & Khursheed, S. (2022). Computer Vision for Hardware Trojan Detection on a PCB Using Siamese Neural Network. 2022 IEEE Physical Assurance and Inspection of Electronics, https://doi.org/10.1109/PAINE56030.2022.10014967
    34. Rosli, N.S., Fauadi, M., Awang, N.F., Noor, A.Z.M. (2018). Vision-based defects detection for glass production based on improved image processing method. Journal of Advanced Manufacturing Technology, 12(1(1)), 203-212.
    35. Rosli, N.S., Fauadi, M., Awang, N. (2016). Some technique for an Image of defect in inspection process based on image processing. Journal of Image and Graphics, 4(1), 55-58.
    36. Schubeck, J., Koblah, D., Botero, U. J., & Forte, D. (2021). A Comprehensive Taxonomy of PCB Defects. University of Florida, Gainesville, USA.
    37. Silva, L. H. D. S., Azevedo, G. O. D. A., Fernandes, B. J. T., Bezerra, B. L. D., Lima, E. B., & Oliveira, S. C. (2019). Automatic Optical Inspection for Defective PCB Detection Using Transfer Learning. 2019 IEEE Latin American Conference on Computational Intelligence. https://doi.org/10.1109/LA-CCI47412.2019.9037036
    38. Sinha, A., & Senapati, K. K. (2023). Analysing Satellite Images using Segmentation with U-net and Focal Tversky Loss. In Proceedings of 2023 6th International Conference on Information Systems and Computer Networks. https://doi.org/10.1109/ISCON57294.2023.10112138
    39. Sun, Y., Sun, Z., & Chen, W. (2024). The evolution of object detection methods. Engineering Applications of Artificial Intelligence, 133, 108458. https://doi.org/10.1016/J.ENGAPPAI.2024.108458
    40. Terven, J., Cordova-Esparza, D. M., Ramirez-Pedraza, A., & Chavez-Urbiola, E. A. (2023). Loss Functions and Metrics in Deep Learning. https://arxiv.org/abs/2307.02694v2
    41. Udaya Sankar, V., Lakshmi, · Gayathri, & Siva Sankar, · Y. (2022). A Review of Various Defects in PCB. Journal of Electronic Testing 1, 3. https://doi.org/10.1007/s10836-022-06026-7
    42. Wang, H., Pan, C., Guo, X., Ji, C., & Deng, K. (2021). From object detection to text detection and recognition: A brief evolution history of optical character recognition. Wiley Interdisciplinary Reviews: Computational Statistics, 13(5), e1547. https://doi.org/10.1002/WICS.1547
    43. Wang, Z., Wang, E., & Zhu, Y. (2020). Image segmentation evaluation: a survey of methods. Artificial Intelligence Review, 53(8), 5637–5674. https://doi.org/10.1007/S10462-020-09830-9/METRICS
    44. Xia, S., Wang, F., Xie, F., Huang, L., Wang, Q., & Ling, X. (2021). An Efficient and Robust Target Detection Algorithm for Identifying Minor Defects of Printed Circuit Board Based on PHFE and FL-RFCN. Frontiers in Physics, 9, 661091. https://doi.org/10.3389/FPHY.2021.661091/BIBTEX
    45. Yang, S., Xiao, W., Zhang, M., Guo, S., Zhao, J., & Shen, F. (2022). Image Data Augmentation for Deep Learning: A Survey. https://arxiv.org/abs/2204.08610v2
    46. Yu, X., Lyu, W., Zhou, D., Wang, C., & Xu, W. (2022). ES-Net: Efficient Scale-Aware Network for Tiny Defect Detection. IEEE Transactions on Instrumentation and Measurement, 71. https://doi.org/10.1109/TIM.2022.3168897
    47. Zeng, N., Wu, P., Wang, Z., Li, H., Liu, W., & Liu, X. (2022). A Small-Sized Object Detection Oriented Multi-Scale Feature Fusion Approach with Application to Defect Detection. IEEE Transactions on Instrumentation and Measurement, 71. https://doi.org/10.1109/TIM.2022.3153997
    48. Zhang, L., Chen, J., Chen, J., Wen, Z., & Zhou, X. (2024). LDD-Net: Lightweight printed circuit board defect detection network fusing multi-scale features. Engineering Applications of Artificial Intelligence, 129, 107628. https://doi.org/10.1016/J.ENGAPPAI.2023.107628
    49. Zhang, Q., & Liu, H. (2021). Multi-scale defect detection of printed circuit board based on feature pyramid network. In Proceedings of 2021 IEEE International Conference on Artificial Intelligence and Computer Applications, 911–914. https://doi.org/10.1109/ICAICA52286.2021.9498174
    50. Zhang, W., Zhu, Q., Li, Y., & Li, H. (2023). MAM Faster R-CNN: Improved Faster R-CNN based on Malformed Attention Module for object detection on X-ray security inspection. Digital Signal Processing, 139, 104072. https://doi.org/10.1016/J.DSP.2023.104072
    51. Zhao, K., Han, Q., Zhang, C. Bin, Xu, J., & Cheng, M. M. (2022). Deep Hough Transform for Semantic Line Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(9), 4793–4806. https://doi.org/10.1109/TPAMI.2021.3077129
    52. Zhou, J., Liu, Y., Zhang, X., & Yang, Z. (2021). Multi-view based template matching method for surface defect detection of circuit board. Journal of Physics: Conference Series, 1983(1), 012063. https://doi.org/10.1088/1742-6596/1983/1/012063
    53. Zhu, Y., Detig, C., Kane, S., & Lourie, G. (2022). Kinematic Motion Analysis with Volumetric Motion Capture. In Proceedings of International Conference on Information Visualisation, 2022-July, 61–66. https://doi.org/10.1109/IV56949.2022.00019
    54. Zhuang, F., Qi, Z., Duan, K., Xi, D., Zhu, Y., Zhu, H., Xiong, H., & He, Q. (2021). A Comprehensive Survey on Transfer Learning. In Proceedings of the IEEE, 109(1), 43–76. https://doi.org/10.1109/JPROC.2020.3004555
    55. Zou, Z., Chen, K., Shi, Z., Guo, Y., & Ye, J. (2023). Object Detection in 20 Years: A Survey. In Proceedings of the IEEE, 111(3), 257–276. https://doi.org/10.1109/JPROC.2023.3238524
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Copyright (c) 2025 The Authors

How to cite

Jian, T. S., Fauadi, M. H. F. M., Yahaya, S. H., Noor, A. Z. M., & Saptari, A. (2024). A deep learning approach for automated PCB defect detection: A comprehensive review. Multidisciplinary Reviews, 8(1), 2025011. https://doi.org/10.31893/multirev.2025011
Crossref
Scopus
Google Scholar
Europe PMC
  • Article viewed - 2244
  • PDF downloaded - 999