Abstract
Printed Circuit Board (PCB) is a significant component of the power system, and their surface defects may hinder electrical performance. Therefore, developing an efficient and precise PCB surface defect detection method is crucial for ensuring the state of the entire power system. In recent years, there has been growing interest in lightweight attention mechanisms that aim to achieve high accuracy with minimal computational cost.In this work, a single-stage object detection network based on YOLO v5m is proposed, which incorporates and compare 3 attention mechanisms to enhance the detection capabilities of the model, namely Coordinate Attention (CA), Convolutional Block Attention Module (CBAM), and Squeeze-and-Excitation (SE), In addition, the evaluation indicator Wise IoU (WIoU) has also been used to replace traditional IoU. Experimental results indicate that the proposed approach achieves mean Average Precision (mAP) of 97.8% and a frame rate of 80.1.Surpassing the performance of other compared models. The proposed approach has the potential to be deployed on edge device in the future.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Yu, L.: Research on analysis and evaluation method of cleaner production in printed circuit board industry. Huazhong University of Science and Technology (2012)
Pal, A., Chauhan, S., Bhardwaj, S.C.: Detection of bare PCB defects by image subtraction method using machine vision. In: Proceedings of the World Congress on Engineering 2011, vol. II 2191 (2011)
Ray, S., Mukherjee, J.: A hybrid approach for detection and classification of the defects on printed circuit board. Int. J. Comput. Appl. 121 (2015)
Reshadat, V., Kapteijns, R.A.: Improving the performance of automated optical inspection (AOI) using machine learning classifiers. In: 2021 International Conference on Data and Software Engineering (ICoDSE), pp. 1–5. IEEE (2021)
Akhtar, M.B.J.H., Journal, I.: The use of a convolutional neural network in detecting soldering faults from a printed circuit board assembly. Hi Tech. Innov. J. 3, 1–14 (2022)
Richter, J., Streitferdt, D.: Modern architecture for deep learning-based automatic optical inspection. In: 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC), pp. 141–145. IEEE (2019)
Huang, S.-H., Pan, Y.-C.J.: Automated visual inspection in the semiconductor industry: a survey. 66, Comput. Indust. 66, 1–10 (2015)
Khasawneh, A.M.: Automation of Surface Mount Technology (SMT) Defects Detection and Classification at Automated Optical Inspection (Aoi) Using Convolutional Neural Network. State University of New York at Binghamton (2019)
Li, C.-J., Qu, Z., Wang, S.-Y., Bao, K.-h., Wang, S.-Y.J.: A method of defect detection for focal hard samples PCB based on extended FPN model. IEEE Ttrans. Compon. Pack. Manuf. Technol. 12, 217–227 (2021)
Wang, H., Xie, J., Xu, X., Zheng, Z.: Few-Shot PCB surface defect detection based on feature enhancement and multi-scale fusion. J. Intergr. Agric. 10, 129911–129924 (2022)
Chen, W., Huang, Z., Mu, Q., Sun, Y.: PCB defect detection method based on transformer-YOLO. J. Intergr. Agric. 10, 129480–129489 (2022)
Ding, R., Dai, L., Li, G., Liu, H.:TDD‐net: a tiny defect detection network for printed circuit boards. CAA Trans. Intell. Technol. 4, 110–116 (2019)
Zou, Z., Chen, K., Shi, Z., Guo, Y., Ye, J.: Object detection in 20 years: a survey. Proc. IEEE (2023)
Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132–7141 (2018)
Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: CBAM: convolutional block attention module. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 3–19. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01234-2_1
Hou, Q., Zhou, D., Feng, J.: Coordinate attention for efficient mobile network design. In: Proceedings of the IEEE/CVF Conference On Computer Vision and Pattern Recognition, pp. 13713–13722. (2021)
Agarwal, S., Terrail, J.O.D., Jurie, F.: Recent advances in object detection in the age of deep convolutional neural networks (2018)
Sharma, V., Mir, R.N.: A comprehensive and systematic look up into deep learning based object detection techniques: a review. Comput. Sci. Revi. 38, 100301 (2020)
Jacobs, R.: Deep learning object detection in materials science: Current state and future directions. Comput. Mater. Sci. 211, 111527 (2022)
Aziz, L., Salam, M.S.B.H., Sheikh, U.U., Ayub, S.: Exploring deep learning-based architecture, strategies, applications and current trends in generic object detection: a comprehensive review. IEEE Access 8, 170461–170495 (2020)
Tong, Z., Chen, Y., Xu, Z., Yu, R.: Wise-IoU: bounding box regression loss with dynamic focusing mechanism (2023)
Diwan, T., Anirudh, G., Tembhurne, J.V.: Applications: object detection using YOLO: challenges, architectural successors, datasets and applications. Multim. Tools Appl. 82, 9243–9275 (2022)
Zheng, Z., Wang, P., Liu, W., Li, J., Ye, R., Ren, D.: Distance-IoU loss: Faster and better learning for bounding box regression. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 12993–13000. (2020)
Zheng, W., Tang, W., Chen, S., Jiang, L., Fu, C.-W.: CIA-SSD: Confident IOU-aware single-stage object detector from point cloud. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 3555–3562 (2021)
Zhai, H., Cheng, J., Wang, M.: Rethink the IoU-based loss functions for bounding box regression. In: 2020 IEEE 9th Joint International Information Technology and Artificial Intelligence Conference (ITAIC), pp. 1522–1528. IEEE, (2020)
Zaidi, S.S.A., Ansari, M.S., Aslam, A., Kanwal, N., Asghar, M., Lee, B.J.: A survey of modern deep learning based object detection models. Digit. Sig. Process. 126, 103514 (2022)
Acknowledgment
This work was supported by the National Natural Science Foundation of China (Nos. 62172004, 62072002, and 61872004), Educational Commission of Anhui Province (No. KJ2019ZD05).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Pan, L. et al. (2023). Efficient and Precise Detection of Surface Defects on PCBs: A YOLO Based Approach. In: Huang, DS., Premaratne, P., Jin, B., Qu, B., Jo, KH., Hussain, A. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2023. Lecture Notes in Computer Science, vol 14087. Springer, Singapore. https://doi.org/10.1007/978-981-99-4742-3_50
Download citation
DOI: https://doi.org/10.1007/978-981-99-4742-3_50
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-4741-6
Online ISBN: 978-981-99-4742-3
eBook Packages: Computer ScienceComputer Science (R0)