Abstract
Defects appeared in the printed circuit board (PCB) will pose a serious damage on the following procedure. Image based inspection methods have been proposed to improve the efficiency and reliability of PCB defect detection compared to manual inspection. The machine learning and deep learning detection methods are popular one, however, they are complex, time consumption and require lots of labeled samples. Thus, we conduct the PCB defect detection and classification by using the template-based algorithm. To realize an accurate registration, the region of interest (ROI) among input image is first computed by utilizing the Grab Cut method. Furthermore, to ensure the complete overlap of feature points between the test image and template image, a perspective transformation based on four vertexes calculating is introduced. Once the different shape and posture images are transformed into a uniform imaging plane, a subtraction operation is used to extract the features of various defects. Experiments on a public data set prove the efficiency of our proposed method.
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References
Ling, Z.G., Zhang, A., Ma, D., Shi, Y.X., Wen, H.: Deep siamese semantic segmentation network for PCB welding defect detection. IEEE Trans. Instrum. Meas. 71, 5006511 (2022)
Mamidi, J.S.S.V., Sameer, S., Bayana, J.: A light weight version of PCB defect detection system using YOLO V4 Tiny. In: 2022 International Mobile and Embedded Technology Conference (MECON), Noida, India, pp. 441–445 (2022)
Li, Z., Yang, Q.: System design for PCB defects detection based on AOI technology. In: 2011 4th International Congress on Image and Signal Processing, Shanghai, China, pp. 1988–1991 (2011)
Borthakur, M., Latne, A., Kulkarni, P.: A comparative study of automated pcb defect detection algorithms and to propose an optimal approach to improve the technique. Int. J. Comput. Appli. 114(6), 27–33 (2015)
Li, M.K., Yao, N.F., Li, S.Q., Zhao, Y.Q., Kong, S.G.: Multisensor image fusion for automated detection of defects in printed circuit boards. IEEE Sens. J. 21(20), 23390–23399 (2021)
Zhang, Z.Q., Wang, X.D., Liu, S., Sun, L., Chen, L.Y., Guo, Y.M.L: An automatic recognition method for PCB visual defects. In: 2018 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC), Xi’an, China, pp138–142 (2018)
Luo, J.X., Chen X.C., Hu, Y.M.: A fast circle detection method based on threshold segmentation and validity check for FPC images. In: 2017 Chinese Automation Congress (CAC), Jinan, China, pp. 3214–3217 (2017)
Dai, L.H., Guan, Q., Liu, H.: Robust image registration of printed circuit boards using improved SIFT-PSO algorithm. J. Eng. 16, 1793–1797 (2018)
Hassanin, A.-A., Abd El-Samie, F.E., El Banby, G.M.: A real-time approach for automatic defect detection from PCBs based on SURF features and morphological operations. Multimedia Tools Appli. 78(24), 34437–34457 (2019). https://doi.org/10.1007/s11042-019-08097-9
Putera, S.H.I., Ibrahim, Z.: Printed circuit board defect detection using mathematical morphology and MATLAB image processing tools. In: 2010 2nd International Conference on Education Technology and Computer, Shanghai, China (2010)
Srimani, P.K., Prathiba, V.: Adaptive data mining approach for PCB defect detection and classification. Indian J. Sci. Technol. 9(44), 1–9 (2016)
Zhang, Z.Q., Wang, X.D., Liu, S., Sun, L., Chen, L.Y., Guo, Y.M.: An automatic recognition method for PCB visual defects. In: 2018 International Conference on Sensing, Diagnostics, Prognostics and Control (SDPC), Xi’an, China (2018)
Ibrahim, Z., Al-Attas, S.A.R., Aspar, Z., Mokji, M.M.: Performance evaluation of wavelet-based PCB defect detection and localization algorithm. In: 2002 IEEE International Conference on Industrial Technology, Bankok, Thailand (2002)
Ding, R.W., Dai, L.H., Li, G.P., Liu, H.: TDD-net: a tiny defect detection network for printed circuit boards. CAAI Trans. Intell. Technol. 4(2), 110–116 (2019)
Adibhatla, V.A., Chih, H.C., Hsu, C.C., Cheng, J., Abbod, M.F., Shieh, J.S.: Defect detection in printed circuit boards using you-only-look-once convolutional neural networks. Electronics 9(9), 1–16 (2020)
Zhang, H.A., Jiang, L.X., Li, C.Q.: Cs-resnet: cost-sensitive residual convolutional neural network for PCB cosmetic defect detection. Expert Syst. Appl. 185, 115673 (2021)
Nguyen, V. T., Bui, H. A.: A real-time defect detection in printed circuit boards applying deep learning. EUREKA: Phys. Eng. 2, 143–153 (2022)
Kim, J., Ko, J., Choi, H., Kim, H.: Printed circuit board defect detection using deep learning via a skip-connected convolutional autoencoder. Sensors 21(15), 4968 (2021)
Tang, S. N., He, F., Huang, X. L., Yang, J.: Online PCB defect detector on a new PCB defect dataset (February 2019)
Huang, W.B., Wei, P.: A PCB dataset for defects detection and classification. J. Latex Class Files 14(8), 1–9 (2018)
Acknowledgements
This work was supported by the Natural Science Foundation of Xiamen under Grant 3502Z20227189.
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Shao, G., Sun, Q., Gao, F., Liu, T., Luo, J., Wei, Y. (2023). An Improved Perspective Transformation and Subtraction Operation for PCB Defect Detection. In: Hong, W., Weng, Y. (eds) Computer Science and Education. ICCSE 2022. Communications in Computer and Information Science, vol 1811. Springer, Singapore. https://doi.org/10.1007/978-981-99-2443-1_13
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DOI: https://doi.org/10.1007/978-981-99-2443-1_13
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