Focusing on strip steel surface defects classification, a novel support vector machine with adjustable hyper-sphere （AHSVM） is formulated. Meanwhile, a new multi-class classification method is proposed. Originated f...Focusing on strip steel surface defects classification, a novel support vector machine with adjustable hyper-sphere （AHSVM） is formulated. Meanwhile, a new multi-class classification method is proposed. Originated from support vector data description, AHSVM adopts hyper-sphere to solve classification problem. AHSVM can obey two principles： the margin maximization and inner-class dispersion minimization. Moreover, the hyper-sphere of AHSVM is adjustable, which makes the final classification hyper-sphere optimal for training dataset. On the other hand, AHSVM is combined with binary tree to solve multi-class classification for steel surface defects. A scheme of samples pruning in mapped feature space is provided, which can reduce the number of training samples under the premise of classification accuracy, resulting in the improvements of classification speed. Finally, some testing experiments are done for eight types of strip steel surface defects. Experimental results show that multi-class AHSVM classifier exhibits satisfactory results in classification accuracy and efficiency.展开更多
Defect classification is the key task of a steel surface defect detection system.The current defect classification algorithms have not taken the feature noise into consideration.In order to reduce the adverse impact o...Defect classification is the key task of a steel surface defect detection system.The current defect classification algorithms have not taken the feature noise into consideration.In order to reduce the adverse impact of feature noise,an anti-noise multi-class classification method was proposed for steel surface defects.On the one hand,a novel anti-noise support vector hyper-spheres(ASVHs)classifier was formulated.For N types of defects,the ASVHs classifier built N hyper-spheres.These hyper-spheres were insensitive to feature and label noise.On the other hand,in order to reduce the costs of online time and storage space,the defect samples were pruned by support vector data description with parameter iteration adjustment strategy.In the end,the ASVHs classifier was built with sparse defect samples set and auxiliary information.Experimental results show that the novel multi-class classification method has high efficiency and accuracy for corrupted defect samples in steel surface.展开更多
A proper detection and classification of defects in steel sheets in real time have become a requirement for manufacturing these products,largely used in many industrial sectors.However,computers used in the production...A proper detection and classification of defects in steel sheets in real time have become a requirement for manufacturing these products,largely used in many industrial sectors.However,computers used in the production line of small to medium size companies,in general,lack performance to attend real-time inspection with high processing demands.In this paper,a smart deep convolutional neural network for using in real-time surface inspection of steel rolling sheets is proposed.The architecture is based on the state-of-the-art SqueezeNet approach,which was originally developed for usage with autonomous vehicles.The main features of the proposed model are:small size and low computational burden.The model is 10 to 20 times smaller when compared to other networks designed for the same task,and more than 700 times smaller than general networks.Also,the number of floating-point operations for a prediction is about 50 times lower than the ones used for similar tasks.Despite its small size,the proposed model achieved near-perfect accuracy on a public dataset of 1800 images of six types of steel rolling defects.展开更多
文摘Focusing on strip steel surface defects classification, a novel support vector machine with adjustable hyper-sphere （AHSVM） is formulated. Meanwhile, a new multi-class classification method is proposed. Originated from support vector data description, AHSVM adopts hyper-sphere to solve classification problem. AHSVM can obey two principles： the margin maximization and inner-class dispersion minimization. Moreover, the hyper-sphere of AHSVM is adjustable, which makes the final classification hyper-sphere optimal for training dataset. On the other hand, AHSVM is combined with binary tree to solve multi-class classification for steel surface defects. A scheme of samples pruning in mapped feature space is provided, which can reduce the number of training samples under the premise of classification accuracy, resulting in the improvements of classification speed. Finally, some testing experiments are done for eight types of strip steel surface defects. Experimental results show that multi-class AHSVM classifier exhibits satisfactory results in classification accuracy and efficiency.
基金This work was supported by the National Natural Science Foundation of China(No.51674140)Natural Science Foundation of Liaoning Province,China(No.20180550067)+2 种基金Department of Education of Liaoning Province,China(Nos.2017LNQN11 and 2020LNZD06)University of Science and Technology Liaoning Talent Project Grants(No.601011507-20)University of Science and Technology Liaoning Team Building Grants(No.601013360-17).
文摘Defect classification is the key task of a steel surface defect detection system.The current defect classification algorithms have not taken the feature noise into consideration.In order to reduce the adverse impact of feature noise,an anti-noise multi-class classification method was proposed for steel surface defects.On the one hand,a novel anti-noise support vector hyper-spheres(ASVHs)classifier was formulated.For N types of defects,the ASVHs classifier built N hyper-spheres.These hyper-spheres were insensitive to feature and label noise.On the other hand,in order to reduce the costs of online time and storage space,the defect samples were pruned by support vector data description with parameter iteration adjustment strategy.In the end,the ASVHs classifier was built with sparse defect samples set and auxiliary information.Experimental results show that the novel multi-class classification method has high efficiency and accuracy for corrupted defect samples in steel surface.
文摘A proper detection and classification of defects in steel sheets in real time have become a requirement for manufacturing these products,largely used in many industrial sectors.However,computers used in the production line of small to medium size companies,in general,lack performance to attend real-time inspection with high processing demands.In this paper,a smart deep convolutional neural network for using in real-time surface inspection of steel rolling sheets is proposed.The architecture is based on the state-of-the-art SqueezeNet approach,which was originally developed for usage with autonomous vehicles.The main features of the proposed model are:small size and low computational burden.The model is 10 to 20 times smaller when compared to other networks designed for the same task,and more than 700 times smaller than general networks.Also,the number of floating-point operations for a prediction is about 50 times lower than the ones used for similar tasks.Despite its small size,the proposed model achieved near-perfect accuracy on a public dataset of 1800 images of six types of steel rolling defects.
