End-to-end Billet Identification Number Recognition System

Koo, Gyogwon; Yun, Jong Pil; Lee, Sang Jun; Choi, Hyeyeon; Kim, Sang Woo

doi:10.2355/isijinternational.isijint-2018-506

Cited by 11 publications

(4 citation statements)

References 14 publications

(12 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The famous CNNs extract good representation by training with ImageNet which has a large data set [28]. Therefore, many studies used the pre-trained models as initial values of the networks to accelerate their convergence in semantic segmentation tasks [17], [21], [25], [29]. The decoder generates the segmented output from the features of the encoder.…”

Section: Automated Brittle Fracture Rate Estimatormentioning

confidence: 99%

“…Deep learning has lately shown great performance in a variety of fields such as industrial application [10]- [12], medical diagnosis [13], [14], agriculture [15] as well as imaging [16]. Semantic segmentation is one of the important tasks in field of the computer vision [17]- [19]. Unlike the classification and object detection tasks, the semantic segmentation task, which is also called as pixel-wise classification, is suitable for identifying and estimating the BFR.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automated Brittle Fracture Rate Estimator for Steel Property Evaluation Using Deep Learning After Drop-Weight Tear Test

et al. 2019

Self Cite

View full text Add to dashboard Cite

This study proposes an automated brittle fracture rate (BFR) estimator using deep learning. As the demand for line-pipes increases in various industries, the need for BFR estimation through dropweight tear test (DWTT) increases to evaluate steel's property. Conventional BFR or ductile fracture rate (DFR) estimation methods require an expensive 3D scanner. Alternatively, a rule-based approach is used with a single charge-coupled device (CCD) camera. However, it is sensitive to the hyper-parameter. To solve these problems, we propose an approach based on deep learning that has recently been successful in the fields of computer vision and image processing. The method proposed in this study is the first to use deep learning approach for BFR estimation. The proposed method consists of a VGG-based U-Net (VU-Net) which is inspired by U-Net and fully convolutional network (FCN). VU-Net includes a deep encoder and a decoder. The encoder is adopted from VGG19 and transferred with a pre-trained model with ImageNet. In addition, the structure of the decoder is the same as that of the encoder, and the decoder uses the feature maps of the encoder through concatenation operation to compensate for the reduced spatial information. To analyze the proposed VU-Net, we experimented with different depths of networks and various transfer learning approaches. In terms of accuracy used in real industrial application, we compared the proposed VU-Net with U-Net and FCN to evaluate the performance. The experiments showed that VU-Net was the accuracy of approximately 94.9 %, and was better than the other two, which had the accuracies of about 91.8 % and 93.7 %, respectively.

show abstract

Section: Automated Brittle Fracture Rate Estimatormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automated Brittle Fracture Rate Estimator for Steel Property Evaluation Using Deep Learning After Drop-Weight Tear Test

et al. 2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…Billet number is a string of characters sprayed on the surface of the billet, generally composed of letters and digits. Billet number typically includes a significant amount of information, such as production batch, spraying date, cast steel type and so on, which is used for billet management and quality monitoring in production 2) . Recognizing billet numbers accurately is an important basis for setting the plate rolling process in the hot rolling mill.…”

Section: Introductionmentioning

confidence: 99%

Recognition of Hot-Cast Billet Numbers Based on ICRNN

Yi,

Liu,

Huang

et al. 2024

ISIJ Int.

View full text Add to dashboard Cite

In steel production, the recognition of hot-cast billet numbers suffers from low efficiency and susceptibility to misjudgment. This paper proposes a novel method for identifying hot-cast billet numbers on the basis of improved Convolutional Recurrent Neural Network (ICRNN). Although the existing CRNN has achieved acceptable results in text recognition and music symbol recognition, it is not effective in recognizing industrial characters that are blurred or low-contrast. Based on the theoretical framework of CRNN, the Grayscale Spatial Transformation Network (GSTN) is added before character recognition to rectify the skew caused by shooting angles. The backbone network for feature extraction is changed to ResNet50. Moreover, the Efficient Channel Attention (ECA) module is added to construct the Res-ECA network, which extracts more features of the billet number characters. In the sequence modeling stage, the Bidirectional Gated Recurrent Unit (BiGRU) is used to reduce the risk of overfitting and accelerate convergence. After experimental comparison on a self-made billet number dataset, the put forward ICRNN has faster recognition speed and higher accuracy, with a recognition accuracy of 99.49%, which is 4.8% higher than that of the CRNN. The result fully demonstrates that ICRNN meets the requirements of accuracy and speed for billet number recognition.

show abstract

“…Text recognition is imperative in this industry, and DL models are used to recognize billet identification numbers (BINs). BINs contain information related to the material of the billet, which is tailored to the customers' needs [6]. As the characteristics of the billet depend on its material, the incorrect recognition and entry of even a single BIN can cause significant financial loss.…”

mentioning

confidence: 99%

Two-Stage Billet Identification Number Recognition Using Label Distribution

Jang,

Choi,

Kim

et al. 2023

IEEE Access

Self Cite

View full text Add to dashboard Cite

With the progressive automation of factories, the demand for deep learning methods capable of recognizing characters is rising. A billet identification number (BIN) is a string of characters that contains all information about the billet, but it is often oriented arbitrarily. Because each plant has different features of data, it requires time and effort to secure enough data to train the model that can be applied to each plant. In addition, the existing BIN recognition model confuses characters with similar shapes when rotated because it shares a feature extractor for angle estimation and character recognition. In this study, we propose a method to solve the problems and improve the BIN recognition performance. We separate the two parts of extracting angles and characters, allowing each module to independently focus on the features of the data. Label distribution is used to enhance the angle estimation accuracy with a small dataset, and the triangular distribution results in the highest accuracy. Finally, to train rotated characters, a large amount of data that are randomly rotated is required, but by separating the angle and character module, the variation within classes is reduced, resulting in high BIN recognition performance even with a small dataset.

show abstract

End-to-end Billet Identification Number Recognition System

Cited by 11 publications

References 14 publications

Automated Brittle Fracture Rate Estimator for Steel Property Evaluation Using Deep Learning After Drop-Weight Tear Test

Automated Brittle Fracture Rate Estimator for Steel Property Evaluation Using Deep Learning After Drop-Weight Tear Test

Recognition of Hot-Cast Billet Numbers Based on ICRNN

Two-Stage Billet Identification Number Recognition Using Label Distribution

Contact Info

Product

Resources

About