Defects Rapid Identification of Marking Region Based on Binarization and BP Neural Network

Jian Chen; Jin Wang; Guo Dong Lu; Xiao Ming Jiang

doi:10.4028/www.scientific.net/AMR.482-484.726

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 482-484Defects Rapid Identification of Marking Region...

Defects Rapid Identification of Marking Region Based on Binarization and BP Neural Network

Abstract:

The status that domestic technology forming the high-pressure gas bottles surface leading to the poor marking quality is analyzed. The rapid image processing method based on Binarization is presented to identify the defects position and profile from the marking region quickly, and it is designed with four steps: CCD capture, pixel enhancement, edge identification and feature extractions. By the statistical analysis of the project practice, the defects is defined as four typical types in shape, and then through the BP neural network training to identify the defects type effectively and driving the machine pre-set coping strategies to make appropriate responses automatically without manual interaction. The final test shows that the automatic high-efficiency marking defects identification is achieved

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Periodical:

Advanced Materials Research (Volumes 482-484)

Pages:

726-731

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.482-484.726

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Online since:

February 2012

Authors:

Jian Chen, Jin Wang, Guo Dong Lu, Xiao Ming Jiang

Keywords:

Binarization, BP Neural Network (BPNN), High-Pressure Gas Bottles, Marking Region, Rapid Identification

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