Abstract
The automatic detection and recognition of automotive wheel hubs defects has important significance to improve the quality and efficiency of automotive wheel production and vehicle safety. In order to improve accuracy of detection and recognition of automotive wheel hub defect images, an improved peak location algorithm - trend peak algorithm is proposed to extract region of wheel hub defect, combined with BP neural network to classify and recognize wheel hub defect. Firstly, initial defect positions are extracted using peak locations of vertical and horizontal directions. Then mathematical morphology is used to remove pseudo defects, and the exact locations of the defects are obtained. Finally, the wheel hub defect features are classified to reach the target of defect recognition by BP neural network. In actual industrial conditions, the algorithm is found to obtain good recognition results and reach real-time detection request in low contrast, high noise, uneven illumination, and complex structure of the products, by experiments of X-ray images of four common defects of the actual wheel hubs.
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Acknowledgements
This work was supported by the National Natural Science Foundation of China under Grant No. 61573157, by the Fund of the Natural Science Foundation of Guangdong Province of China under Grant No. 2014A030313454, and by the Natural Science Foundation of Jiangxi, China (No. 20142BAB217028).
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Li, W., Li, K., Huang, Y., Deng, X. (2016). A New Trend Peak Algorithm with X-ray Image for Wheel Hubs Detection and Recognition. In: Li, K., Li, J., Liu, Y., Castiglione, A. (eds) Computational Intelligence and Intelligent Systems. ISICA 2015. Communications in Computer and Information Science, vol 575. Springer, Singapore. https://doi.org/10.1007/978-981-10-0356-1_3
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DOI: https://doi.org/10.1007/978-981-10-0356-1_3
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