Research On Image Classification Method Of Steel Surface Defects Based On Self-paced Learning

Surface defects of steel are essential factors that determine the quality of steel products.In the process of steel production,product quality inspection is a crucial stage.The automatic classification of steel surface defects is a key section in the intelligent analysis system for the quality of steel products,which is essential for the high-quality development of steel companies.This paper takes the surface defect images of steel products of a large iron and steel enterprise as the object,and combines advanced theories of machine learning and deep learning to conduct an in-depth study on the classification method of steel surface defects based on self-paced learning,and realize automatic classification of steel surface defect images.The main research work is as follows:(1)Aiming at the problem of automatic classification of steel surface defect images,this paper proposes a method of steel surface defect image classification based on a self-paced supporting vector machine.According to the characteristics of steel surface defects,this method first constructs a defect feature pool with strong discriminability,and realizes the accurate representation of steel surface defects.On this basis,the current advanced self-paced learning method and support vector machine classification method are combined to construct a self-paced support vector machine classification model.Aiming at the learning problem of this model,this paper designs an ACS(Alternative Convex Search)method to realize the learning process of the classification model ’from simple to difficult’ and improve the accuracy of defect classification.In order to verify the validity of the method,a steel surface defect data set was constructed.A large number of comparative experimental results show that the method can achieve the accuracy of steel surface defect classification,and self-paced learning can help improve the classification performance of support vector machines.(2)Aiming at the problem of high cost of image marking on steel surface defects,an unsupervised clustering method for steel surface defects based on self-paced learning is proposed.This method combines a self-paced learning method with a Gaussian mixture model,and proposes a self-paced diversity Gaussian mixture clustering model.Aiming at the learning problem of model parameters,an efficient alternative search strategy has been implemented.This strategy can guarantee to learn from ’easy to difficult’ while also ensuring the diversity of learning requirements.The experimental results show that the clustering performance of the self-paced Gaussian mixture clustering method is superior to the traditional Gaussian mixture model.(3)Aiming at the difficulty in designing the surface defect features of steel,an image classification method for steel surface defects based on self-paced Densenet is proposed.This method combines a self-paced learning method with a deep neural network,and proposes a selfpaced Densenet classification model.Aiming at the learning problem of model parameters,an efficient alternative search strategy was implemented.This strategy can automatically learn the defect features and realize the learning process of model parameters from simple to difficult.Experimental results show that this method can achieve better classification performance than Densenet,and also has good robustness.