Brake pad image classification algorithm basedon color segmentation and information entropy weighted feature matching

doi:10.16511/j.cnki.qhdxxb.2018.26.025

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Abstract In appearance inspection of car brake pads based on machine vision, the segmentation of the brake pad image and the extraction and matching of the shape features are keys of brake pads classification. In order to realize high-precision shape classification, this paper proposes a brake pad image classification algorithm including foreground segmentation, geometric feature selection and template matching. First the RGB image captured by the camera is converted to HSV color space and using saturation channel the brake pad is segmented from the dark belt background. Then the multi-dimensional geometric features of the area are extracted. Finally, an improved feature matching algorithm based on information entropy weighting is proposed, in which Manhattan distance of the feature space is weighted by the information entropy of the brake pad features. The experimental results show that the accuracy of the algorithm is 95.00%, and the average processing time is 110 ms. It can be applied to the real-time automatic classification procedure during brake manufacturing.

Keywords brake linings machine vision hue saturation value (HSV) color space geometric feature information entropy

Issue Date: 15 June 2018

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	ZHAO Lei
	ZHANG Wen
	SUN Zhenguo
	CHEN Qiang

Cite this article:

ZHAO Lei,ZHANG Wen,SUN Zhenguo, et al. Brake pad image classification algorithm basedon color segmentation and information entropy weighted feature matching[J]. Journal of Tsinghua University(Science and Technology), 2018, 58(6): 547-552.

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http://jst.tsinghuajournals.com/EN/10.16511/j.cnki.qhdxxb.2018.26.025 OR http://jst.tsinghuajournals.com/EN/Y2018/V58/I6/547

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