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Application of instance segmentation algorithm incorporating attention mechanism and BiFPN for sinter ore particle size recognition

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Název: Application of instance segmentation algorithm incorporating attention mechanism and BiFPN for sinter ore particle size recognition
Autoři: Zhe Li, Tao Xue, Jie Li, Aimin Yang
Zdroj: Ironmaking & Steelmaking: Processes, Products and Applications. 51:1010-1022
Informace o vydavateli: SAGE Publications, 2024.
Rok vydání: 2024
Témata: 0203 mechanical engineering, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology
Popis: The particle size composition of sinter plays a decisive role in the output and energy consumption of blast furnace ironmaking. To reduce energy consumption and achieve efficient production, accurate measurement of sinter particle size and distribution has become an important issue. Due to the problems of adhesion, large size difference and edge effect in sinter image, the traditional image segmentation algorithm makes it difficult to effectively separate sinter. This paper proposes an intelligent recognition method of sinter particle size based on an improved BlendMask instance segmentation algorithm. Bidirectional feature pyramid network is used to replace the feature pyramid network structure (FPN), and the attention mechanism Convolutional Block Attention Module (CBAM) is introduced to improve the ability to extract shallow features and enhance the ability to identify adherent sinter. The test results show that the average detection accuracy mean average precision (mAP) of the model proposed in this article is 71.0%, and the mAP50 is 93.8%. The average segmentation accuracy mAP is 68.0% and mAP50 is 93.8%. The identified sinter images were then edge-refined using morphological methods to collect statistics on the particle size distribution of the sinter. Compared with the actual measured sinter particle size, the average relative error is 4.9%. This method can accurately identify sinter in complex environments, improve the production efficiency of sinter, optimise resource utilisation and reduce personnel costs and has important application prospects.
Druh dokumentu: Article
Jazyk: English
ISSN: 1743-2812
0301-9233
DOI: 10.1177/03019233241266294
Rights: URL: https://journals.sagepub.com/page/policies/text-and-data-mining-license
Přístupové číslo: edsair.doi...........68dacb34be292fef12d7f722aeba7adc
Databáze: OpenAIRE
Popis
Abstrakt:The particle size composition of sinter plays a decisive role in the output and energy consumption of blast furnace ironmaking. To reduce energy consumption and achieve efficient production, accurate measurement of sinter particle size and distribution has become an important issue. Due to the problems of adhesion, large size difference and edge effect in sinter image, the traditional image segmentation algorithm makes it difficult to effectively separate sinter. This paper proposes an intelligent recognition method of sinter particle size based on an improved BlendMask instance segmentation algorithm. Bidirectional feature pyramid network is used to replace the feature pyramid network structure (FPN), and the attention mechanism Convolutional Block Attention Module (CBAM) is introduced to improve the ability to extract shallow features and enhance the ability to identify adherent sinter. The test results show that the average detection accuracy mean average precision (mAP) of the model proposed in this article is 71.0%, and the mAP50 is 93.8%. The average segmentation accuracy mAP is 68.0% and mAP50 is 93.8%. The identified sinter images were then edge-refined using morphological methods to collect statistics on the particle size distribution of the sinter. Compared with the actual measured sinter particle size, the average relative error is 4.9%. This method can accurately identify sinter in complex environments, improve the production efficiency of sinter, optimise resource utilisation and reduce personnel costs and has important application prospects.
ISSN:17432812
03019233
DOI:10.1177/03019233241266294