基于环形特征的卷积神经网络轮毂识别

doi:10.3969/j.issn.1000-1158.2022.06.05

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Abstract Aiming at the mixed-flow production of different types of wheels, a wheel hub identification algorithm of convolutional neural networks based on ring features is proposed. The circular hub in rectangular coordinates is mapped to polar coordinates, normalized to a rectangle in standard form, and the feature information is extracted by rotation to reduce the influence of redundant features. An improved VGG network architecture is designed, which uses depthwise separable convolution to break the relationship between the output channel dimension and the size of the convolution kernel. It reduces the computation without losing the network performance. The hub recognition algorithm is evaluated in terms of effectiveness and real-time performance, and through comparative experiments of models such as Inception V3, SVM, and KNN etc. The experiment shows that the method has a processing accuracy of more than 99%, and the processing time of a single image is reduced to 11.78ms.

Key words： metrology wheel model identification ring features image normalization convolutional neural networks depthwise separable convolution

Received: 07 January 2021 Published: 30 June 2022

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TB96

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	Articles by authors
	CHENG Shu-hong
	LU Jia-xin
	ZHANG Dian-fan
	XU Nan

Cite this article:

CHENG Shu-hong,LU Jia-xin,ZHANG Dian-fan, et al. Wheel Hub Identification of Convolutional Neural Networks Based on Ring Features[J]. Acta Metrologica Sinica, 2022, 43(6): 729-735.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2022.06.05 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2022/V43/I6/729

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