Application of Efficient Channel Attention Residual Mechanism in Blast Furnace Tuyere Image Anomaly Detection
In the steelmaking industry, the state of the blast furnace tuyere is an important basis for obtaining the internal information of the blast furnace. Traditional detections mainly rely on manual experience judgment, which is a time-consuming and tiring procedure for a human. In order to improve the...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/15/7823 |
| _version_ | 1797414557533077504 |
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| author | Rihong Wang Ziyu Li Lingzhi Yang Yuming Li Hao Zhang Chuanwang Song Mingjian Jiang Xiaoyun Ye Keyong Hu |
| author_facet | Rihong Wang Ziyu Li Lingzhi Yang Yuming Li Hao Zhang Chuanwang Song Mingjian Jiang Xiaoyun Ye Keyong Hu |
| author_sort | Rihong Wang |
| collection | DOAJ |
| description | In the steelmaking industry, the state of the blast furnace tuyere is an important basis for obtaining the internal information of the blast furnace. Traditional detections mainly rely on manual experience judgment, which is a time-consuming and tiring procedure for a human. In order to improve the efficiency of the detection, this paper is devoted to applying artificial intelligence methods to blast furnace anomaly detection. However, because of the low imaging degree of the abnormal state monitoring of the furnace mouth, the difference in the abnormal category is inconspicuous, and it is difficulty to extract the features with the existing intelligent models. To solve these problems, a novel and stable method is proposed in this paper to classify the image recognition of the abnormal state of the tuyere into one category; this is a new architecture that combines multiple technologies. For the fine-grained image classification task, an improved abnormal state recognition algorithm of the blast furnace tuyere based on the channel attention residual mechanism is proposed. In the model, the dataset is augmented by rotating it at random angles to balance the amount of data in each category; then, the residual module is used to integrate high- and low-order feature information and optimize the network; then, the multi-layer channel attention module is added based on the channel attention residual mechanism, and it obtains the optimal parameter combination of the model through k-fold cross-validation. Moreover, the number of channels was reduced by half after channel fusion, which could effectively reduce the model parameters and model complexity. It is shown in our experiments that the proposed method has an accuracy rate of 97.10% in identifying the abnormal state of the tuyere in our collection of blast furnace tuyere datasets. In order to test the performance of the proposed method, some existing models, such as SERNet, ResNeXt, and repVGG, are involved for comparison, and the proposed method has a better classification effect in comparison to them. |
| first_indexed | 2024-03-09T05:35:03Z |
| format | Article |
| id | doaj.art-f544020b029348fc95ed476f7cba2670 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T05:35:03Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-f544020b029348fc95ed476f7cba26702023-12-03T12:29:38ZengMDPI AGApplied Sciences2076-34172022-08-011215782310.3390/app12157823Application of Efficient Channel Attention Residual Mechanism in Blast Furnace Tuyere Image Anomaly DetectionRihong Wang0Ziyu Li1Lingzhi Yang2Yuming Li3Hao Zhang4Chuanwang Song5Mingjian Jiang6Xiaoyun Ye7Keyong Hu8School of Information and Control Engineering, Qingdao University of Technology, Qingdao 266520, ChinaSchool of Information and Control Engineering, Qingdao University of Technology, Qingdao 266520, ChinaQingdao Special Iron and Steel Co., Ltd., Qingdao 266409, ChinaSchool of Information and Control Engineering, Qingdao University of Technology, Qingdao 266520, ChinaSchool of Information and Control Engineering, Qingdao University of Technology, Qingdao 266520, ChinaSchool of Information and Control Engineering, Qingdao University of Technology, Qingdao 266520, ChinaSchool of Information and Control Engineering, Qingdao University of Technology, Qingdao 266520, ChinaSchool of Information and Control Engineering, Qingdao University of Technology, Qingdao 266520, ChinaSchool of Information and Control Engineering, Qingdao University of Technology, Qingdao 266520, ChinaIn the steelmaking industry, the state of the blast furnace tuyere is an important basis for obtaining the internal information of the blast furnace. Traditional detections mainly rely on manual experience judgment, which is a time-consuming and tiring procedure for a human. In order to improve the efficiency of the detection, this paper is devoted to applying artificial intelligence methods to blast furnace anomaly detection. However, because of the low imaging degree of the abnormal state monitoring of the furnace mouth, the difference in the abnormal category is inconspicuous, and it is difficulty to extract the features with the existing intelligent models. To solve these problems, a novel and stable method is proposed in this paper to classify the image recognition of the abnormal state of the tuyere into one category; this is a new architecture that combines multiple technologies. For the fine-grained image classification task, an improved abnormal state recognition algorithm of the blast furnace tuyere based on the channel attention residual mechanism is proposed. In the model, the dataset is augmented by rotating it at random angles to balance the amount of data in each category; then, the residual module is used to integrate high- and low-order feature information and optimize the network; then, the multi-layer channel attention module is added based on the channel attention residual mechanism, and it obtains the optimal parameter combination of the model through k-fold cross-validation. Moreover, the number of channels was reduced by half after channel fusion, which could effectively reduce the model parameters and model complexity. It is shown in our experiments that the proposed method has an accuracy rate of 97.10% in identifying the abnormal state of the tuyere in our collection of blast furnace tuyere datasets. In order to test the performance of the proposed method, some existing models, such as SERNet, ResNeXt, and repVGG, are involved for comparison, and the proposed method has a better classification effect in comparison to them.https://www.mdpi.com/2076-3417/12/15/7823blast furnace tuyereresidual networkattention mechanismtuyere imageimage recognition |
| spellingShingle | Rihong Wang Ziyu Li Lingzhi Yang Yuming Li Hao Zhang Chuanwang Song Mingjian Jiang Xiaoyun Ye Keyong Hu Application of Efficient Channel Attention Residual Mechanism in Blast Furnace Tuyere Image Anomaly Detection Applied Sciences blast furnace tuyere residual network attention mechanism tuyere image image recognition |
| title | Application of Efficient Channel Attention Residual Mechanism in Blast Furnace Tuyere Image Anomaly Detection |
| title_full | Application of Efficient Channel Attention Residual Mechanism in Blast Furnace Tuyere Image Anomaly Detection |
| title_fullStr | Application of Efficient Channel Attention Residual Mechanism in Blast Furnace Tuyere Image Anomaly Detection |
| title_full_unstemmed | Application of Efficient Channel Attention Residual Mechanism in Blast Furnace Tuyere Image Anomaly Detection |
| title_short | Application of Efficient Channel Attention Residual Mechanism in Blast Furnace Tuyere Image Anomaly Detection |
| title_sort | application of efficient channel attention residual mechanism in blast furnace tuyere image anomaly detection |
| topic | blast furnace tuyere residual network attention mechanism tuyere image image recognition |
| url | https://www.mdpi.com/2076-3417/12/15/7823 |
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