Automatic Detection of Surface Defects in Industrial Materials Based on Image Processing

  • Authors

    • R Srividhya
    • K Shanmugapriya
    • K Sindhu Priya
    https://doi.org/10.14419/ijet.v7i3.34.18717

    Received date: September 1, 2018

    Accepted date: September 1, 2018

    Published date: September 1, 2018

  • FCM, SVM, Defect detection

  • Abstract

    In the field of industry, corrosion and defects are amongst the most frequent operations. Industrial Materials have periodic defects that are difficult to detect during production even by experienced human inspectors. Defects are difficult to detect during production even by experienced human inspectors. Usually, the colour transfer process contains an image segmentation phase and an image construction phase. Therefore, we introduce an image processing method for automatically detecting the defects in surfaces. We show how barely visible defect can be optically enhanced to improve annual assessment as well as how descriptor-based image processing and machine learning can be used to allow automated detection. Image enhancement is performed by applying manual calculation. We implement this simulation using MATLAB R2013a. Results show that the proposed allows training both tested classifiers with good classification rates around 98.9%.

  • References

    1. S.H. Bhandari, S. M. Deshpande, A Simple Approach to Surface Defect Detection , IEEE Region 10 Colloquium and the Third International Industrial and Information Systems, Kharagpur, INDIA February, 2017, 8-10.
    2. Y. C. Samarawickrama Chamira D. Wickramasinghe, Matlab based automated surface defect detection system for ceremic tiles using image processing, National Conference on Technology and Man-agement , 2017.
    3. R. Giblas, T. Sentenac ,J. J. Orteu, M. Sauvee, G. Sierra, F. Gacoin, O. Guiwlaumon, Detection of micro cracks on highly specular re-flectors:Dimensioning a vision machine based on optical properties, IEEE Sensors Journal, 17, 23, 2017.
    4. L. Ma’rifatul Azizah, S. Fadillah Umayah, S. Riyadi, C. Damarjati, Deep learning implementation using convolutional neural network in mangoteen surface defect detection, 7th IEEE International Confer-ence on Control System, Computing and Engineering (ICCSCE 2017), 2017, 24-26
    5. K. Liu, H. Wang, H. Chen, E. Qu, Y. Tian, H. Sun , Steel surface defect detection using a new haar-weibull-variance model in unsu-pervised manner, IEEE transactions on instrumentation and meas-urement,2017.
    6. M. S. Sayed , Robust Fabric Defect Detection Algorithm Using En-tropy Filtering and Minimum Error Thresholding, IEEE 59th Inter-national Midwest Symposium on Circuits and Systems (MWSCAS), 2016, 16-19.
    7. M. E. Stivanello, S. Vargas, M. L. Roloff and M. R. Stemmer, Au-tomatic Detection and Classification of Defects in Knitted Fabrics, IEEE Latin America Transactions, 14, 7, 2016.
    8. J. Ke, C. Duan, Wei Yi, C. Yan, Progress In Electromagnetic Re-search Symposium (PIERS), 2016 8-11.
    9. Y. Zhang, D. Lefebvre, and Q. Li, Automatic Detection of Defects in Tire Radiographic Images, IEEE Transactions On Automation Science And Engineering, 2015.
    10. T. Rocha, H. Ramos, A. Lopes Ribeiro, D. Pasadas, Sub-Surface Defect Detection with Motion Induced Eddy Currents in Alumini-um, IEEE Instrumentation and Measurement Society, 2015.
    11. D. Ming Tsai, Y. H. Tseng, W.Y. Chire, Surface defect detection in low contrast images using basis image representation, 14th Interna-tional Conference on Machine Vision Applications (ICMVA) 2015, 18-22.
    12. M. Z. B. Edris, M.S. Jawad, Surface defect detection and Neural Network recognition of automotive body panels, IEEE Internation-al Conference on Control System, Computing and Engineering, 2015, 27-29.
    13. M. Molodova, Z. Li, Alfredo N ez, Automatic detection of Squats in railway infrastructure, 16th International IEEE Conference on In-telligent Transportation Systems, 2013, 6-9.
    14. M. S. Biradar, S. P. Jadhav, Fabric Defect Detection by using Neu-ral Network technique, International Conference on Computing Communication Control and Automation 2014.
    15. V. V. Karlekar, M.S.Biradar and K.B.Bhangale, Fabric Defect De-tection using Wavelet Filter, International Conference on Compu-ting Communication Control and Automation, 2015.
    16. L. Xiao-cong, A hybrid SVM-QPSO model based ceramic tube sur-face defect detection algorithm, Fifth International Conference on Intelligent Systems Design and Engineering Applications, 2014.
    17. W. Zeng, H. Tao Wang, G.Y. Tian, et.al, Application of non contact laser ultrasonic imaging technique in surface defect detection IEEE Transaction, 2014.
    18. S. Ghorai, A. Mukherjee, M. Gangadaran, P. K. Dutaa, Automatic defect detection on hot rolled flat steel product, IEEE Transactions On Instrumentation and Measurement, 62, 3, 2013.
    19. M. Eftekhari, M. Moallem, S. Sadri, A. Shojaei, Review of Induc-tion Motor Testing and Monitoring Methods for Inter-turn Stator Winding Faults, IEEE Transactions on Instrumentation and Meas-urement, 62, 3, 2013.
    20. V. Agilandeswari, J. Anuja, E. D. George, Fabric Quality Testing using Image Processing, ICICES 2014.

  • Downloads

  • How to Cite

    Srividhya, R., Shanmugapriya, K., & Sindhu Priya, K. (2018). Automatic Detection of Surface Defects in Industrial Materials Based on Image Processing. International Journal of Engineering and Technology, 7(3.34), 61-64. https://doi.org/10.14419/ijet.v7i3.34.18717