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.
