Abstract
Bundled bars counting is difficult in the cases of overlap or varied illumination. An iteratively trained SVM method is proposed to count bundled round bars from a bottom side image. Using Hough transformation, the sizes of bars are extracted and normalized. A SVM classifier using HOG features of the image are applied to determine the center points of bars. These center points generate central regions corresponding to bars. By counting the number of connected regions with great area in the image, the number of bars is obtained. In SVM training process, sample selection affects the classifier significantly. From an iteratively selection process, typical samples are selected and used for training the SVM classifier. The experimental results showed this strategy improved the performance of SVM classifier effectively, and the method works well in overlapped or varied illumination situation.
Supported by Provincial Natural Science Foundation of Liaoning Province, China. (Grant No. 20170540792).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Okumoto, M., Nakamura, S.: Algorithm to automatically count the number of steel pipes. J. Fukui Natl. Coll. Technol. 41, 25–28 (2007)
Liu, X.H., Ouyang, J.N.: Research on steel bar detection and counting method based on contours. In: Proceedings International Conference on Electronics Technology, Chengdu, China, pp. 294–297 (2018)
Wang, J.Z., Chen, H., Xu, X.Q.: Pattern recognition for counting of bounded bar steel. In: Proceedings 4th International Conference on the Applications of Digital Information and Web Technologies, Stevens Point, Wisconsin, USA, pp. 173–176 (2011)
Wu, Y., Zhou, X.F., Zhang, Y.C.: Steel bars counting and splitting method based on machine vision. In: Proceedings 5th Annual IEEE International Conference on Cyber Technology in Automation, Shenyang, China, pp. 420–425 (2015)
Ghazali, M.F., Wong, L.-K., See, J.: Automatic detection and counting of circular and rectangular steel bars. In: Ibrahim, H., Iqbal, S., Teoh, S.S., Mustaffa, M.T. (eds.) 9th International Conference on Robotic, Vision, Signal Processing and Power Applications. LNEE, vol. 398, pp. 199–207. Springer, Singapore (2017). https://doi.org/10.1007/978-981-10-1721-6_22
Xing, Y., Xue, W., Yuan, P.X., et al.: Research on an automatic counting method for steel bars’ image. In: Proceedings International Conference on Electrical & Control Engineering, Wuhan, China, pp. 1644–1647 (2010)
Zhao, J.Y., Xia, X.X., Wang, H.D., et al.: Design of real-time steel bars recognition system based on machine vision. In: Proceedings 8th International Conference on Intelligent Human-Machine Systems and Cybernetics, Hangzhou, China, pp. 505–509 (2016)
Hou, W.Y., Duan, Z.W., Liu, X.D.: A template-covering based algorithm to count the bundled steel bars. In: Proceedings 4th International Congress on Image and Signal Processing, Shanghai, China, pp. 1813–1916 (2011)
Yan, X., Chen, X.Q.: Research on the counting algorithm of bundled steel bars based on the features matching of connected regions. In: Proceedings 3rd International Conference on Image, Vision and Computing, Chongqing, China, pp. 11–15 (2018)
Llorca, D.F., Arroyo, R., Miguel, Á.S.: Vehicle logo recognition in traffic images using HOG features and SVM. In: Proceedings Intelligent Transportation Systems Conference, Hague, Netherlands, pp. 2229–2234 (2013)
Yao, C., Wu, F., Sun, H.J., et al.: Traffic sign recognition using HOG-SVM and grid search. In: Proceedings 12th International Conference on Signal Processing, Hangzhou, China, pp. 962–965 (2014)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proceedings International Conference on Computer Vision and Pattern Recognition, San Diego, USA, pp. 886–893 (2005)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Liu, C., Zhu, L., Zhang, X. (2019). Bundled Round Bars Counting Based on Iteratively Trained SVM. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Application. ICIC 2019. Lecture Notes in Computer Science(), vol 11643. Springer, Cham. https://doi.org/10.1007/978-3-030-26763-6_15
Download citation
DOI: https://doi.org/10.1007/978-3-030-26763-6_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-26762-9
Online ISBN: 978-3-030-26763-6
eBook Packages: Computer ScienceComputer Science (R0)