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第40届中国控制会议

作者： Qizi Huangpeng Xiaojun Duan Wenwei Huang College of Arts and Science National University of Defense Technology College of Basic Military Education National University of Defense Technology

Defect classification is an important part of automated surface defect detection ***,the types of surface defects of industrial products are complex and diverse,and the correct defect type classification can help to subsequently extract the characteristic of ***,defect classification can help to count the number of defect modes automatically and provide data support for the precise maintenance of product *** to the large number of surface defects and the small difference between defect types,traditional classification methods are difficult to classify defect ***,in order to improve the accuracy of defect image classification,this paper proposes a defect image classification method based on transfer learning and sparse ***,a deep CNN feature extraction algorithm for defect images is proposed in combination with transfer ***,the deep CNN features of the defect image are dimension-reduced and sparsely optimized using the sparse coding techniques,and the sparse CNN features are ***,the sparse CNN features are classified to realize the defect type determination using the linear *** accuracy of the proposed method is verified by using a steel surface defect image benchmark database,and the effectiveness of the proposed method is proved.

关键词： surface defects classification Transfer Learning Sparse Coding

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A Smart Deep Convolutional Neural Network for Real-Time surface Inspection

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Computer Systems Science & Engineering 2022年第5期41卷 583-593页

作者： Adriano G.Passos Tiago Cousseau Marco A.Luersen Department of Mechanical Engineering Federal University of TechnologyParanaCuritiba81280-340PRBrazil

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 ***,computers used in the production line of small to medium size companies,in general,lack performance to attend real-time inspection with high processing *** this paper,a smart deep convolutional neural network for using in real-time surface inspection of steel rolling sheets is *** architecture is based on the state-of-the-art SqueezeNet approach,which was originally developed for usage with autonomous *** main features of the proposed model are:small size and low computational *** 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 ***,the number of floating-point operations for a prediction is about 50 times lower than the ones used for similar *** its small size,the proposed model achieved near-perfect accuracy on a public dataset of 1800 images of six types of steel rolling defects.

关键词： Deep learning surface defects classification steel rolling

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