International Journal of Precision Engineering and Manufacturing

Korean Society for Precision Engineering (한국정밀공학회)
권호 표지

Real-Time Pipe Fault Detection System Using Computer Vision

  • Kim Hyoung-Seok (School of Mech. & Auto. Engineering, University of Ulsan) ;
  • Lee Byung-Ryong (School of Mech. & Auto. Engineering, University of Ulsan)
  • Published : 2006.01.01
Download PDF
⟨ Previous Next ⟩

Abstract

Recently, there has been an increasing demand for computer-vision-based inspection and/or measurement system as a part of factory automation equipment. In general, it is almost impossible to check the fault of all parts, coming from part-feeding system, with only manual inspection because of time limitation. Therefore, most of manual inspection is applied to specific samples, not all coming parts, and manual inspection neither guarantee consistent measuring accuracy nor decrease working time. Thus, in order to improve the measuring speed and accuracy of the inspection, a computer-aided measuring and analysis method is highly needed. In this paper, a computer-vision-based pipe inspection system is proposed, where the front and side-view profiles of three different kinds of pipes, coming from a forming line, are acquired by computer vision. And the edge detection is processed by using Laplace operator. To reduce the vision processing time, modified Hough transform is used with clustering method for straight line detection. And the center points and diameters of inner and outer circle are found to determine eccentricity of the parts. Also, an inspection system has been built so that the data and images of faulted parts are stored as files and transferred to the server.

Keywords

References

  1. Otsu, N., 'A Threshold seleciton Method from Gray Level Histograms,' IEEE Trans. Syst. Man Cybernet., Vol. SMC-9, pp. 62-66. 1979
  2. Park, H. J., Hwan, Y. M., 'Dimensional Measurement Using the Machine Vision,' KSPE, Vol. 18, No. 3, pp.10-17, 2001
  3. Maitre, H., 'Contribution to the prediction of prediction of performance of the Hough transform,' IEEE Trans. On Patten Analysis and Machine Intelligence., PAMI-8. 5, 1986
  4. Braggins, D. W., 'Image processing for industrial applications in Europe,' Proceedings of the 5th International Conference on Robot Vision and Sensory Control. pp. 13-23
  5. Persoon, E., 'A Pipelied Analysis System Using Custom Integrated Circuits,' IEEE On Patten Analysis and Machine Intelligence, Vol. 10. No. 1, pp. 110-116, 1988 https://doi.org/10.1109/34.3874
  6. Gotoh, T., Toriu, T., Sasaki, S., 'A Flexible Vision-Based Algorithm for a Book Sorting System,' IEEE On Patten Analysis and Machine Intelligence, Vol. 10, No. 3, pp. 393-399, 1988 https://doi.org/10.1109/34.3903
  7. Shapiro, S. D., 'Transform for the Computer Detection of Curves in Noisy Picture,' Comp. Gragpics Image Process. 4, pp. 328-338. 1975 https://doi.org/10.1016/0146-664X(75)90002-7
  8. Ballad, D. H., 'Generalizing the Hough Transform to Detect Arbitrary shapes,' Pattern Recongniton, 13, pp. 111-121, 1981 https://doi.org/10.1016/0031-3203(81)90009-1
  9. Leavers, V. F., 'The dynamic Generalized Hough Transform,' First ECCV conf. Antibes, France, 1990
  10. Califano, A., Tayler, R. W., 'A new approach to complex Parameter Feature Extraction,' IEEE On Patten Analysis and Machine Intelligence, pp. 192-199. 1989