Aluminum Profile Type Recognition Based on Texture Features

Abstract

Because aluminum profile’s structure is complex and diverse, we need to match the different parameters for different profiles before automated detection of surface defects of aluminum profile. This process often requires manual input, affecting the detection efficiency. To solve this problem, we analyze the characteristics of aluminum profile, through GLCM algorithm and Gabor wavelet transform methods, which are image texture feature extraction methods to get aluminum profile’s texture feature, then we use the Support Vector Machine (SVM) classification algorithm based on radial basis function (RBF) core classify the feature, for the aim of matching parameters automatically. By feature extraction time and the recognition accuracy rate and other indicators to compare the experimental results of each method, derived using Gabor wavelet transform is the best both on recognition accuracy or identify time effects, and can satisfy the actual needs.