Research On Multi-Space Classification Model And Recognition Technology Of Strip Defect Image

As the market changes and restructuring, high-quality sheet steel in the steel industry's position is becoming increasingly important. Its comsuption rapidly increasing in automotive, home appliance, shipbuilding, aerospace and other important applications concerned. As results coming from continuous casting billets, rolling equipment, relavent process, and many other reasons, there are weld, inclusion, wrinkles, scale, roller mark, scratches and various defects in strip surface. With these defections, the corrosion resistance, wear resistance, fatigue strength and other important performance of the product is reduced; on the other hand, it can not be used in the scopes with appearance quality demanded any more. As the world's biggest iron and steel production country, plenty of high-quality steel strip is imported in China, every year. It becomes an important strategic significance to improve the surface quality of strip.Pattern recognition is an important step in detection technology based on the image information. Currently pattern recognition technology which is used in strip surface defect detection system has a lot problem such as complex recognition steps, low recognition rate, slow recognition speed, weak generalization ablility, poor adaptability and so on. The reason is mainly due to: the characteristics of the study, on one side, the image of different types of defects does not exist the clear boundaries in the characteristics space, on the other hand, the character of same type of defects might be really different; on common pattern classification methods, the classification mechanism and the characteristics of classes distribution in feature space exist in a variety of inconsistent in the way of expressions, scale, expansion feature and so on which produced the problem such as the situations of miscellaneous points around the cluster center, fitting surface divided into same class samples and so on, causeing higher possibility of error. In view of the above problems, it is possible by constructing a good model of adaptive classification process to be addressed. The classification model and the specific realization of the classifier is focused on for further improvment in the study.The research content and results are as follows:(1) Though the in-depth research of the pattern classification mechanism, the common problems and limitations of current classification methods is analysed. To solve these problems, the concept concerned classes eigen space and cognitive space are proposed, and then the multi-space classification model which has better consistency of the class distribution of cognitive space with the basic elements of many single type expansion subspace is established. The construction method is given for this model. Through theoretical analysis and case analysis shows that the superiority of the classification mechanism comparing with the common methods.(2) According to the requirments of the mulit-space classification model, WTM-SOFM classification method is proposed as the optimization of SOFM neural network. The method used to track the training history of SOFM network approach to overcome the less capacity of SOFM neural network for classification of the situations of cramped and idel space among classes centre in the feature space. The experimental study shows that it is a classifier with more cpapcity of the conflict mediation and the border expansion compared to SOFM.(3) proposed a new pattern classification method, namely, the territory classification method. This method is elicited from the multi-space division effect in the formation of the territory of the history of mankind, based on the request of mulit-space classification model. Comparing the method of WTM-SOFM, the division and estimation rules for the unknown classification space are more reasonable. Experimental analysis shows it can still show very high recognition rate and recognition speed on the situations of the complex boundary staggered, multi-scale and other issues which is difficult to solve or impossible to calculate for some comon ways. In the experiment of strip surface defects classification, the identification of the six defects was significantly higher than other common classification methods.