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EL-DenseNet: a novel method for identifying the flame state of converter steelmaking based on dense convolutional neural networks

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Abstract

The identification of flame status in converter steelmaking is of great significance for steel smelting and molten steel quality. It can monitor the converter smelting process, strictly control the smooth progress of the production process, and effectively avoid personal injury. However, converter steelmaking is located in a production environment with high temperatures, high smoke, and strong physical and chemical reactions. The results of traditional manual fire observation are influenced by various factors such as experience and environmental conditions, showing unstable classification results, resulting in low accuracy in identifying the flame state of converter steelmaking. In order to improve the accuracy of flame state recognition in converter steelmaking, this article fully utilizes the flame image information provided by a certain steelmaking company during the converter steelmaking blowing process to classify the converter steelmaking flame images. This article proposes a novel dense convolutional neural network (EL-DenseNet) model for flame state recognition in converter steelmaking. Firstly, the efficient channel attention mechanism (ECAM) was introduced into DenseBlock to enhance the model’s attention to different channels, locate relevant useful information, and suppress useless information, thereby improving the model’s ability to capture key features in flame images; Then, in the model training and validation stage, LabelSmoothing was used to replace the original cross entropy loss function, smoothing the real labels and reducing overfitting of the model to the training data. Through experiments, it has been shown that the training accuracy of the EL-DenseNet model proposed in this article is 99\(\%\), and the testing accuracy is 96.7\(\%\). This improves the accuracy of flame state recognition in converter steelmaking, saves manpower and material resources, and reflects the effectiveness and superiority of the model.

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Availability of data and materials

The dataset has been rigorously tested and filtered, and it will soon be made available for open access at https://github.com/JiaTang123/JiaTang-1.

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Funding

This research is partially supported by the Key Technology Research and Collaborative Innovation of Endogenous Security of Industrial Internet. (Grant NO. HZ2021015)

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Authors and Affiliations

  1. Chongqing University of Science and Technology, Huxi Street, Shapingba District, Chongqing, 401331, China

    Yan Hu, Jia Tang, Runying Xu & Baoshan Huang

  2. Chengyu (Chengdu) Information and Communication Research Institute Co., LTD, Wenmiao Back Street, Chengdu, 610200, Sichuan, China

    Yangyang Xu

Authors
  1. Yan Hu
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  2. Jia Tang
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  3. Yangyang Xu
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  4. Runying Xu
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  5. Baoshan Huang
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Contributions

JT and YH wrote the main manuscript text; YYX collected the data set for the experiment and preprocessed the data RYX wrote Figs. 2,  3,  4,  5,  6, B.S.H. wrote Figs. 7,  8,  9,  10, while RYX worked with BSH to find relevant literature for the manuscript.

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Correspondence to Jia Tang.

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Hu, Y., Tang, J., Xu, Y. et al. EL-DenseNet: a novel method for identifying the flame state of converter steelmaking based on dense convolutional neural networks. SIViP 18, 3445–3457 (2024). https://doi.org/10.1007/s11760-024-03011-9

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  • DOI: https://doi.org/10.1007/s11760-024-03011-9

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