Complex Image Recognition Method of Train Wheels Based on Contour Feature

Dong Yao Jia; Po Hu

doi:10.4028/www.scientific.net/AMR.937.409

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HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 937Complex Image Recognition Method of Train Wheels...

Complex Image Recognition Method of Train Wheels Based on Contour Feature

Abstract:

At present, most of the domestic train wheel detection is still manual. Existing image detection methods do not fully consider the characteristics of wheel profiles, as the algorithm is very complicated, they do not apply to fast dynamic monitoring. The paper proposes a dynamic image recognition method of train wheels based on contour feature. Firstly, wheel contour curve is extracted using RCD circular arc detection algorithm with constraint condition. Then, after analyzing the wheel contour curves, vector contour is constructed to describe them. At last, images of wheels are rapid recognized, and the recognition is based on identification function. Experimental results show that this method can achieve 95% accuracy when the similarity of complete wheel is set to 0.98 or more. This method can adapt to railway environment and has a certain application value.

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Periodical:

Advanced Materials Research (Volume 937)

Pages:

409-415

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.937.409

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Online since:

May 2014

Authors:

Dong Yao Jia, Po Hu*

Keywords:

Arc Detection, Contours Potential, Image Acquisition, Image Recognition

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* - Corresponding Author

References

[1] Zheng Shaoping. A Detection System Study of Train Wheel Based on CCD Imaging Technology[D]. Nanchang. Nanchang University. 2010, 1-40(In Chinese).

Google Scholar

[2] Liu Xinghong. Image Features Extraction Based on Similar Contents and Concepts[D]. Chongqing. Chongqing University of Posts and Telecommunications. 2007, 1-67(In Chinese).

Google Scholar

[3] Zhang Fengling. Research on Contour-based Image Matching Technology[D]. Nanjing. Nanjing University of Aeronautics and Astronautics. 2010, 1-66(In Chinese).

Google Scholar

[4] YUAN Li, CAO Zhi-rui. Improved randomized Hough transform algorithm for circle detection[J]. JOURNAL OF COMPUTER APPLICATIONS, 2010, 30(1): 174-176(In Chinese).

Google Scholar

[5] Teh-Chuan Chen, Kuo-Liang Chung. An Efficient Randomized Algorithm for Detecting Circles[J]. Computer Vision and Image Understanding. 2001, 83: 172-191.

DOI: 10.1006/cviu.2001.0923

Google Scholar

[6] CHEN Xue-song, XU Xue-jun, ZHU Hong-bo. Research on Target Contour Feature Extraction Based on Image Potential Energy Theory[J]. COMPUTER SCIENCE, 2011, 38(6): 270-274(In Chinese).

Google Scholar

[7] Kanzawa Y, Ohkawa T, Ito T. Proposal of Low-resolution Vehicle Image Recognition Method[C]. 2008 IEEE Intelligent Vehicles Symposium. 2008: 43-48.

DOI: 10.1109/ivs.2008.4621196

Google Scholar

[8] Wen Pinggeng, Liu Chun, Li Jian. A Smart Image Background Recognition Model and Its Application to Content-Based Image Retrieval[C]. 2010 2nd International Workshop on Intelligent Systems and Application (ISA). 2010: 1-4.

DOI: 10.1109/iwisa.2010.5473357

Google Scholar

[9] Ni Qiakai, Guo Chao, Yang Jing. Research of Face Image Recognition Based on Probabilistic Neural Networks[C]. 2012 24th Chinese Control and Decision Conference. 2012: 3885-3888.

DOI: 10.1109/ccdc.2012.6243102

Google Scholar