PCB Inspection in the Context of Smart Manufacturing

  • Abhishek MukhopadhyayEmail author
  • L. R. D. Murthy
  • Manish Arora
  • Amaresh Chakrabarti
  • Imon Mukherjee
  • Pradipta Biswas
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 134)


Manual assembly of Printed Circuit Board (PCB) in small- and medium-sized enterprises is error-prone and tedious. A small change in orientation of the components might affect the functionality of PCB to an unpredictable extent. We have focused on Automated Optical Inspection (AOI) system for PCB inspection problem in the context of smart manufacturing. The user interface is empowered with bi-directional task—(I) Industrial worker can inspect whether ICs exist or not by comparing with corresponding Gerber file (II) Automatic Inspection of PCB shall be performed in the absence of Gerber file. This is a preliminary step in the direction of automated real-time analysis of PCBs. The work presented in this paper deals with the image acquisition phase of inspection too, which is relatively less investigated when compared to image processing phase. Present automatic inspection is achieved with the histogram of hue values defined in the Hue Saturation Value (HSV) color space. Color distribution-based segmentation is carried out by taking the background color as the dominant value in the histogram. Then in the next step, features extracted from the resultant parts are compared with template ICs to obtain robust results and confirmed ICs are annotated in the live image. We have evaluated the efficiency of the algorithm in four different lighting conditions—outdoor light, LED, CFL, and incandescent lights and observed that 12 W LED works best with 2500 lx illumination. Then with the 12 W LED, we have analyzed accuracy with different camera resolution. We have observed that algorithm works best in the resolution of 1920 × 1080 which is both time consuming and cost-effective.


Shape detection Computer vision PCB inspection Automated optical inspection Image processing 


  1. 1.
    Wang, S., Wan, J., Di, L., Chunhua, Z.: Implementing smart factory of Industrie 4.0: an outlook. Int. J. Distrib. Sens. Netw. 2016(January), e3159805 (2016). Scholar
  2. 2.
    Biswas, P., et al.: Interactive sensor visualization for smart manufacturing system. In: Proceedings of the 31st British Human Computer Interaction Conference 2017 (British HCI 17)Google Scholar
  3. 3.
    Hirano, Y., Garcia, C., Sukthankar, R., Hoogs, A.: Industry and object recognition: applications, applied research and challenges. In: Toward Category-Level Object Recognition, pp. 49–64 (2006)CrossRefGoogle Scholar
  4. 4.
    Moganti, M., Ercal, F., Dagli, C.H., Tsunekawa, S.: Automatic PCB inspection algorithms: a survey. Comput. Vis. Image Underst. 63(2), 287–313 (1996)CrossRefGoogle Scholar
  5. 5.
    Ballard, D.H.: Generalizing the Hough Transform to detect arbitrary shapes. Pattern Recognit. 13(2), 111–122 (1981)CrossRefGoogle Scholar
  6. 6.
    Duda, R.O., Hart, E.: Use of Hough Transformation to detect lines and curves in pictures. Gr. Image Process. 15(1), 11–15 (1972)zbMATHGoogle Scholar
  7. 7.
    Lewis, J.P.: Fast template matching. In: Vision Interface 95, Canadian Image Processing and Pattern Recognition Society, Quebec City, pp. 120–123 (1995)Google Scholar
  8. 8.
    Zhou, Z., Liu, K., Ou, X.: Active contour energy used in object recognition method. In: IEEE Region 10 Conference (TENCON)—Proceeding of the International Conference, pp. 743–745 (2016)Google Scholar
  9. 9.
    Shechtman, E., Irani, M.: Matching local self-similarities across images and videos. In: IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, MN, USA (2007)Google Scholar
  10. 10.
    Wang, W., Chen, S., Chen, L., Chang, W.: A machine vision based automatic optical inspection system for measuring drilling quality of printed circuit boards. IEEE Access 5, 10817–10833 (2017)CrossRefGoogle Scholar
  11. 11.
    Cai, N., Lin, J., Ye, Q., Wang, H., Weng, S., Ling, B.W.: A new IC solder joint inspection method for an automatic optical inspection system based on an improved visual background extraction algorithm. IEEE Trans. Compon. Pack Manuf. Technol. 6, 161–172 (2016)CrossRefGoogle Scholar
  12. 12.
    Sikka, S., Sikka, K., Bhuyan, M.K., Iwahori, Y.: Pseudo vs. True Defect Classification in Printed Circuits Boards Using Wavelet Features. CoRR, abs/1310.6654 (2012)Google Scholar
  13. 13.
    Amin, N., Khadem, M.S.: Interface development for cost effective automated IC orientation checking systems. In: 2007 10th International Conference on Computer and Information Technology, pp. 1–6 (2007)Google Scholar
  14. 14.
    Nagarajan, R., et al.: A real time marking inspection scheme for semiconductor industries. Int. J. Adv. Manuf. Technol. 34, 926–932 (2007)CrossRefGoogle Scholar
  15. 15.
    Basilico, S.: Image-based competitive printed circuit board analysis. In: Stanford Digital Image Processing Projects (2015)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Abhishek Mukhopadhyay
    • 1
    Email author
  • L. R. D. Murthy
    • 1
  • Manish Arora
    • 1
  • Amaresh Chakrabarti
    • 1
  • Imon Mukherjee
    • 2
  • Pradipta Biswas
    • 1
  1. 1.CPDMIndian Institute of ScienceBangaloreIndia
  2. 2.Indian Institute of Information TechnologyKalyaniIndia

Personalised recommendations