Adriano G. Passos, Tiago Cousseau, Marco A. Luersen*
Computer Systems Science and Engineering, Vol.41, No.2, pp. 583-593, 2022, DOI:10.32604/csse.2022.020020
Abstract 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… More >
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