End-to-end Billet Identification Number Recognition System
In steel industry, product number recognition is necessary for factory automation. Before final production, the billet identification number (BIN) should be checked to prevent mixing billets of different material. There are two types of BINs, namely, paint-type and sticker-type BINs. In addition, th...
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Published in | ISIJ International Vol. 59; no. 1; pp. 98 - 103 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
Published |
The Iron and Steel Institute of Japan
15.01.2019
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Subjects | |
Online Access | Get full text |
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Summary: | In steel industry, product number recognition is necessary for factory automation. Before final production, the billet identification number (BIN) should be checked to prevent mixing billets of different material. There are two types of BINs, namely, paint-type and sticker-type BINs. In addition, the BIN comprises seven to nine alphanumeric characters except the letters I and O. The BIN may be rotated in various directions. Therefore, for proper recognition and accident prevention, end-to-end BIN recognition system that uses the deep learning is proposed. Specifically, interpretation and sticker extraction modules are developed. Furthermore, the fully convolutional network (FCN) with deconvolution layer is used and optimized. To increase the BIN recognition accuracy, the FCN was simulated for various structures and was transferred from the pre-trained model. The BIN is identified by the trained FCN model and interpretation module. If the BIN is sticker-type, it is inferred after the sticker region is extracted by the sticker extraction module. The accuracy of the proposed system was shown to be approximately 99.59% in an eight-day period. |
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ISSN: | 0915-1559 1347-5460 |
DOI: | 10.2355/isijinternational.ISIJINT-2018-506 |