Skip to main content
Log in

Automatic inspection of LED indicators on automobile meters based on a seeded region growing algorithm

  • Published:
Journal of Zhejiang University SCIENCE C Aims and scope Submit manuscript

Abstract

Light emitting diode (LED) indicators used on automobile meters are essential for safe driving and few errors can be tolerated. The current manual inspection approach can achieve only 95% accuracy rate in weeding out errors occurring in the production process. It is imperative to improve the accuracy of the inspection process to better achieve the goal of safe driving. This paper proposes an automatic inspection method for LED indicators for use on automobile meters. Firstly, red-green-blue (RGB) color images of LED indicators are acquired and converted into R, G, and B intensity images. A seeded region growing (SRG) algorithm, which selects seeds automatically based on Otsu’s method, is then used to extract the LED indicator regions. Finally, a region matching process based on the seed and three area parameters of each region is applied to inspect the LED indicators one by one to locate any errors. Experiments on standard automobile meters showed that the inspection accuracy rate of this method was up to 99.52% and the inspection speed was faster compared with the manual method. Thus, the new method shows good prospects for practical application.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Adamek, T., O’Connor, N., Murphy, N., 2005. Region-Based Segmentation of Images Using Syntactic Visual Features. 6th Int. Workshop Image Analysis for Multimedia Interactive Services, p.1–4.

  • Adams, R., Bischof, L., 1994. Seeded region growing. IEEE Trans. Pattern Anal. Mach. Intell., 16(6):641–647. [doi:10.1109/34.295913]

    Article  Google Scholar 

  • Buxton, B.F., Abdallahi, H., Delmiro, F.R., Jarra, W., 2007. Development of an Extension of the Otsu Algorithm for Multidimensional Image Segmentation of Thin-Film Blood Slides. Proc. Int. Conf. on Computing: Theory and Applications, p.552–562.

  • Chang, C.Y., Li, C.H., Lin, S.Y., Jeng, M., 2009. Application of two Hopfield neural networks for automatic four-element LED inspection. IEEE Trans. Syst. Man Cybern., 39(3):352–365. [doi:10.1109/TSMCC.2009.2013817]

    Article  Google Scholar 

  • Ghazi Saeidi, R., Latifi, M., Shaikhzadeh Najar, S., Ghazi Saeidi, A., 2005. Computer vision-aided fabric inspection system for on-circular knitting machine. Textile Res. J., 75(6):492–497. [doi:10.1177/0040517505053874]

    Article  Google Scholar 

  • Gonzalez, R.C., Woods, R.E., Eddins, S.L., 2003. Digital Image Processing Using MATLAB. Prentice Hall Inc., NJ, USA, p.195–241.

    Google Scholar 

  • Ikonomatakis, N., Plataniotis, K.N., Zervakis, M., Venetsanopoulos, A.N., 1997. Region Growing and Region Merging Image Segmentation. Proc. 13th Int. Conf. on Digital Signal Processing, p.299–302. [doi:10.1109/ICDSP.1997.628077]

  • Kumar, A., 2008. Computer-vision-based fabric defect detection: a survey. IEEE Trans. Ind. Electron., 55(1):348–363. [doi:10.1109/TIE.1930.896476]

    Article  Google Scholar 

  • Lee, B.Y., Tarng, Y.S., 2001. Surface roughness inspection by computer vision in turning operations. Int. J. Mach. Tools Manuf., 41(9):1251–1263. [doi:10.1016/S0890-6955(01)00023-2]

    Article  Google Scholar 

  • Malamas, E.N., Petrakis, E.G.M., Zervakis, M., Petit, L., Legat, J.D., 2003. A survey on industrial vision system, applications and tools. Image Vis. Comput., 21(2):171–188. [doi:10.1016/S0262-8856(02)00152-X]

    Article  Google Scholar 

  • Moganti, M., Ercal, F., Dagli, C.H., Tsunekawa, S., 1996. Automatic PCB inspection algorithms: a survey. Comput. Vis. Image Understand., 63(2):287–313. [doi:10.1006/cviu.1996.0020]

    Article  Google Scholar 

  • Newman, T.S., Jain, A.K., 1995. A survey of automated visual inspection. Comput. Vis. Image Understand., 61(2):231–262. [doi:10.1006/cviu.1995.1017]

    Article  Google Scholar 

  • Otsu, N., 1979. A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern., 9(1):62–66. [doi:10.1109/TSMC.1979.4310076]

    Article  MathSciNet  Google Scholar 

  • Pal, N.R., Pal, S.K., 1993. A review on image segmentation techniques. Pattern Recogn., 26(9):1277–1294. [doi:10.1016/0031-3203(93)90135-J]

    Article  Google Scholar 

  • Perng, D.B., Chou, C.C., Chen, W.Y., 2007. A novel vision system for CRT panel auto-inspection. J. Chin. Inst. Ind. Eng., 24(5):341–350.

    Google Scholar 

  • Tuduki, Y., Murase, K., Izumida, M., Miki, H., Kikuchi, K., Murakami, K., Ikezoe, J., 2000. Automated Seeded Region Growing Algorithm for Extraction of Cerebral Blood Vessels from Magnetic Resonance Angiographic Data. Proc. 22nd Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society, 3:1756–1759. [doi:10.1109/IEMBS.2000.900424]

    Google Scholar 

  • Wu, H.S., Barba, J., Gil, J., 1996. Region growing segmentation of textured cell image. Electron. Lett., 32(12): 1084–1085. [doi:10.1049/el:19960738]

    Article  Google Scholar 

  • Wu, L.M., Wu, F.J., Wang, G.T., 2008. Computer Vision Inspection for IC Wafer Based on Character of Pixels Distribution. Proc. 3rd Int. Conf. on Convergence and Hybrid Information Technology, 2:248–251. [doi:10.1109/ICCIT.2008.40]

    Google Scholar 

  • Zhang, Y.J., 1996. A survey on evaluation methods for image segmentation. Pattern Recogn., 29(8):1335–1346. [doi:10.1016/0031-3203(95)00169-7]

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hai-er Xu.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Zhou, H., Xu, He., He, Pq. et al. Automatic inspection of LED indicators on automobile meters based on a seeded region growing algorithm. J. Zhejiang Univ. - Sci. C 11, 199–205 (2010). https://doi.org/10.1631/jzus.C0910144

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1631/jzus.C0910144

Key words

CLC number

Navigation