Defects of wheel hubs detection and recognition based on trend peak algorithm
by Wei Li; Kangshun Li; Ying Huang; Xiaoyang Deng
International Journal of Embedded Systems (IJES), Vol. 9, No. 3, 2017

Abstract: In industrial applications, automatic detection 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, the trend peak algorithm, is proposed to extract the region of wheel hub defect, combined with BP neural network to classify and recognise 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.

Online publication date: Wed, 21-Jun-2017

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