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- W4281643044 abstract "Surfaces quality control of rolled products has received great interest from researchers because of its use in many industries including the manufacture of aircraft, cars, and ships. In this context, the quality control process has evolved from relying on human vision, which was characterized by a lot of trouble and errors, to relying on machine vision. In this context, this paper aims to use AlexNet convolutional neural network in order to develop a modified model for the recognition of surface defects in hot rolled steel strip by using the technique of transfer learning. The experimental results of this model provided a classification rate of 98.6%, which is promising results compared to some models issued from recent studies and research work." @default.
- W4281643044 created "2022-06-13" @default.
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- W4281643044 date "2022-05-08" @default.
- W4281643044 modified "2023-09-30" @default.
- W4281643044 title "Automatic surface defect recognition for hot-rolled steel strip using AlexNet convolutional neural network" @default.
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- W4281643044 doi "https://doi.org/10.1109/ispa54004.2022.9786361" @default.
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