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《Modular Machine Tool & Automatic Manufacturing Technique》 2013-04
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Study on Detection System of Bullet Surface Defect Based on Machine Vision

SHI Jin-wei,GUO Chao-yong,LIU Hong-ning(Department of Basic Courses,Ordnance Engineering College,Shijiazhuang 050003,China)  
In order to realize automatic detection of bullet surface defect,the detection system of bullet surface defect based on machine vision is studied.Firstly,according to the image properties of bullet surface detect,the disappointment to segment defect by gray threshold and the only one color model is analyzed,the image segmentation based on hybrid color model is proposed,it is realized that the bullet surface defect is segmented quickly and accurately;Secondly,the properties of bullet surface defect are analyzed,12 distinct defect feature parameters are extracted from shape,color and texture,the feature of bullet surface defect is extracted;Finally,the BP neural network is selected as the classification machine of bullet surface defect,according to the experience and experiment,the structure and parameters of neural network are ensured,after analyzing the disappointment of traditional BP neural network,which is applied in the classification of bullet surface defect,the BP method is optimized by changing the standard of convergence、self adaptive step and adding momentum,the classification effect of neural network is improved.The experiment shows that the defect can be segmented,recognized and classified automatically by the detection system of bullet surface defect,the discriminating rate can reach 91.7%,put up a good ability of accuracy and robustness.
【Fund】: 总装重点预研项目基金资助
【CateGory Index】: TJ410.6;TP391.41
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