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Auto Parts Visual Inspection in Severe Changes in the Lighting Environment
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 Title & Authors
Auto Parts Visual Inspection in Severe Changes in the Lighting Environment
Kim, Giseok; Park, Yo Han; Park, Jong-Seop; Cho, Jae-Soo;
This paper presents an improved learning-based visual inspection method for auto parts inspection in severe lighting changes. Automobile sunroof frames are produced automatically by robots in most production lines. In the sunroof frame manufacturing process, there is a quality problem with some parts such as volts are missed. Instead of manual sampling inspection using some mechanical jig instruments, a learning-based machine vision system was proposed in the previous research[1]. But, in applying the actual sunroof frame production process, the inspection accuracy of the proposed vision system is much lowered because of severe illumination changes. In order to overcome this capricious environment, some selective feature vectors and cascade classifiers are used for each auto parts. And we are able to improve the inspection accuracy through the re-learning concept for the misclassified data. The effectiveness of the proposed visual inspection method is verified through sufficient experiments in a real sunroof production line.
auto parts visual inspection;machine vision;adaboost;kNN;selective feature vector;
 Cited by
CFM에서 하강 에지 정렬과 파라미터 에러 평가에 의한 크림프 시그널 분석 성능 향상,;강태삼;한충권;박정근;

제어로봇시스템학회논문지, 2016. vol.22. 9, pp.686-692 crossref(new window)
G. Kim, S. Lee, and J. S. Cho, "A learning-based visualinspection system for part verification in a panoramasunroof assembly line using the SVM algorithm,"Journal of Institute of Control, Robotics and Systems (inKorean), vol. 19, no. 12, pp. 1099-1104, 2013. crossref(new window)

S. Lee, G. Kim, and J. S. Cho, "Volts inspection of panorama sunroof frame using polar coordinated histogram," The 25th Workshop on Image Processing and Image Understanding (in Korean), p. 106, Jeju, 2013.

C. Cortes and V. N. Vladimir, "Support-vector networks," Machine Learning, 20, 1995.

Y. Freund and R. E. Schapire, "A Short Introduction toBoosting," Journal of Japanese Society for ArtificialIntelligence, vol. 14, no. 5, pp. 771-780, Sep. 1999.

N. Bhatia et al, "Survey of nearest neighbor techniques," International Journal of Computer Science and Information Security, vol. 8, no. 2, Jul. 2010.

N. Dalal and B. Triggs, "Histogram of oriented gradients for human detection," Proc. of CVPR 2005, vol. 1, pp. 886-893, Jun. 2005.

V. Vapnik, The Nature of Statistical Learning Theory, New York: Springer-Verlag, 1995.

X. Liang, "Effective method of pruning support vectormachine classifiers," Neural Networks, IEEE Transactionson, vol. 21, no. 1, pp. 26-38, Dec. 2009.

S. J. Lee and S. W. Kim, "Classifying scrach defects onbillets using image processing and SVM," Journal ofInstitute of Control, Robotics and Systems (in Korean),vol. 19, no. 3, pp. 256-261, Mar. 2013. crossref(new window)