About this Abstract |
Meeting |
2025 TMS Annual Meeting & Exhibition
|
Symposium
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Artificial Intelligence Applications in Integrated Computational Materials Engineering
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Presentation Title |
A Study on the Smoke Recognition of Steelmaking Plants Based on EL-MobileNet |
Author(s) |
Yanming Zhang, Pan Sun, Mujun Long, Zhihuan Wang, Wuguo Chen, Danbin Jia |
On-Site Speaker (Planned) |
Yanming Zhang |
Abstract Scope |
In the converter steelmaking process, automating the control of fog cannon smoke removal is a crucial task in intelligent metallurgy. However, mainstream target classification models encounter limitations in their deployment on on-site mobile monitoring devices and embedded systems due to their high computational and storage costs. To address this, this paper proposes an improved lightweight model based on MobileNet_V2. The efficient channel attention mechanism (ECAM) was introduced and Label Smoothing was used to replace the original cross entropy loss function. The study evaluates the performance of the enhanced lightweight model against mainstream target classification models. Results indicate that the improved EL-MobileNet lightweight model achieves the better performance, with a recognition accuracy of 99.61%, surpassing other models by 1%~2%, while occupying only 3.8MB of memory. The model's memory usage is reduced by over 50%. Application results show the potential of this model to facilitate intelligent control of smoke in steel plants. |
Proceedings Inclusion? |
Planned: |
Keywords |
Environmental Effects, Iron and Steel, Machine Learning |