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