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Open AccessArticle

Periodic Surface Defect Detection in Steel Plates Based on Deep Learning

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Collaborative Innovation Center of Steel Technology, University of Science and Technology Beijing, Beijing 100083, China
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Appl. Sci. 2019, 9(15), 3127; https://doi.org/10.3390/app9153127
Received: 12 June 2019 / Revised: 15 July 2019 / Accepted: 27 July 2019 / Published: 1 August 2019
(This article belongs to the Special Issue Computer Vision and Pattern Recognition in the Era of Deep Learning)
It is difficult to detect roll marks on hot-rolled steel plates as they have a low contrast in the images. A periodical defect detection method based on a convolutional neural network (CNN) and long short-term memory (LSTM) is proposed to detect periodic defects, such as roll marks, according to the strong time-sequenced characteristics of such defects. Firstly, the features of the defect image are extracted through a CNN network, and then the extracted feature vectors are inputted into an LSTM network for defect recognition. The experiment shows that the detection rate of this method is 81.9%, which is 10.2% higher than a CNN method. In order to make more accurate use of the previous information, the method is improved with the attention mechanism. The improved method specifies the importance of inputted information at each previous moment, and gives the quantitative weight according to the importance. The experiment shows that the detection rate of the improved method is increased to 86.2%. View Full-Text
Keywords: periodic defect; deep learning; CNN; LSTM; attention mechanism periodic defect; deep learning; CNN; LSTM; attention mechanism
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Liu, Y.; Xu, K.; Xu, J. Periodic Surface Defect Detection in Steel Plates Based on Deep Learning. Appl. Sci. 2019, 9, 3127.

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