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Parts Surface Structure Image Classification Detection System Design

Journal from gdlhub / 2016-11-11 04:03:05
Oleh : Min Cui, Xiangming Deng, Kuilu Liu, Weiyi Deng, Telkomnika
Dibuat : 2016-09-01, dengan 1 file

Keyword : Surface structure; image detection, feature extraction; BP neural network; classification and recognition
Url : http://journal.uad.ac.id/index.php/TELKOMNIKA/article/view/4398

In order to accomplish the automatic nondestructive testing, a parts surface structure image classification detection system is designed. A series of parts surface texture images have been obtained from different processing methods for feature analysis and the combination of pre-processing method by MATLAB image processing toolbox has been put forward, using statistical analysis method for feature extraction. Based on the established BP neural network training optimization identification system, this paper realized the recognition of parts surface resulted from four kinds of processing methods: turning, milling, planing and grinding. The research results show that the deficit value of gray level co-occurrence matrix and the histogram matrix variance value can be regarded as characteristic parts of the surface texture structure value, providing foundations for further development of parts surface structure detection.

Deskripsi Alternatif :

In order to accomplish the automatic nondestructive testing, a parts surface structure image classification detection system is designed. A series of parts surface texture images have been obtained from different processing methods for feature analysis and the combination of pre-processing method by MATLAB image processing toolbox has been put forward, using statistical analysis method for feature extraction. Based on the established BP neural network training optimization identification system, this paper realized the recognition of parts surface resulted from four kinds of processing methods: turning, milling, planing and grinding. The research results show that the deficit value of gray level co-occurrence matrix and the histogram matrix variance value can be regarded as characteristic parts of the surface texture structure value, providing foundations for further development of parts surface structure detection.

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