Strip Steel Surface Defect Recognition Based on Complex Network Characteristics
基于复杂网络特性的带钢表面缺陷识别

A feature extraction method based on the characteristics of dynamic evolution complex networks is proposed for the strip steel surface defect recognition. The extracted features possess displacement, rotation and size invariability, strong anti-interference ability and robustness, therefore they are good classification features for steel surface defect recognition. In order to improve the efficiency of classification, the principal component analysis (PCA) is adopted to reduce the dimension of the feature vector. The directed acyclic graph support vector machine (DAG-SVM) algorithm is used for the defect classification. The experimental results show that this method is of high recognition rate and fast recognition speed.