A novel method for detecting the type of surface defects of hot rolled steel strip using the convolutional neural network

Steel production is essential in today's world. The classification of surface defects of steel strips in the steel industry is essential for their diagnosis because it is closely related to the quality of the final product. In this study, classification is considered to identify six types of defects from the North Eastern University dataset on hot rolled steel strip surfaces using artificial intelligence (AI). The proposed method is a kind of architecture based on a convolutional neural network. ۲۰۰ × ۲۰۰ images enter the convolutional neural network, changing to ۳۲ × ۳۲ in the first layer, ۶۴ × ۶۴ in the second layer, and ۱۲۸ × ۱۲۸ in the third layer. The test results show that this architecture achieves ۹۳.۵۴% accuracy in the test set, which is much more than comparable architectures. To evaluate the results of the proposed architecture, the criteria of accuracy, precision, and recall have been used.

کلیدواژه ها:

Surface defects ، Hot-rolled steel strip ، Convolutional neural network ، Deep Learning ، Artificial Intelligence