Corrosion Assessment of Carbon Steel Using Texture and Color Features

This paper presents an assessment method for the corrosion evaluation of carbon steel. The digital image processing technology is used to extract the texture and color characteristics of rusted carbon steel images. First, Q235 carbon steel images with different degrees of corrosion were collected by digital camera and microscope. Second, the texture features of rusted carbon steel surface are extracted based on Gray Level Co-occurrence Matrix, the color features are extracted using color moments. Third, the extracted features are combined and normalized as the inputs of Support Vector Machine (SVM) for training and classification. We use Particle Swarm Optimization (PSO) algorithm to optimize the SVM classifier. The accuracy of the classification is up to 97.5%.