基于改进注意力网络的转炉炼钢状态判别  

Discrimination of Converter Steelmaking State Based on Improved Attention Network

作　　者：贺雨霞 曹国[1] HE Yu-xia;CAO Guo(College of Computer Science and Engineering,Nanjing University of Science and Technology,Nanjing 210094,China)

机构地区：[1]南京理工大学计算机科学与工程学院,江苏南京210094

出　　处：《计算机与现代化》2022年第7期97-102,共6页Computer and Modernization

基　　金：国家自然科学基金资助项目(61801222);江苏省自然科学基金资助项目(BK20191284);上海市青年科技英才扬帆计划资助项目(20YF1409300)。

摘　　要：转炉炼钢的状态判别对成品钢材质量的好坏有直接影响。根据人工经验的状态判别需要持续观察炉口的火焰变化,存在主观性强、成本高等问题。为了提升转炉炼钢状态判别的准确率,提出一种基于注意力机制的3D残差卷积神经网络模型。改进的通道注意力将平均池化和最大池化进行特征融合,可以推断出更精细的通道特征,空间注意力能提取到空间上的重点信息。实验结果表明,改进的模型效果好于SE、CBAM和ECA注意力模块,与未加注意力机制的3D残差模型相比,F1分数提高了1.03个百分点,准确度提高了1.06个百分点。最后通过消融实验,分析通道注意力和空间注意力对于网络模型的影响。The status discrimination of converter steelmaking has a direct impact on the quality of finished steel.The manual experience-based state discrimination requires continuous observation of flame changes at the furnace mouth,which is highly subjective and costly.In order to improve the accuracy of the judgment of the converter steelmaking state,a 3D residual convolutional neural network model based on attention mechanism is proposed.The improved channel attention combines average pooling and maximum pooling for feature fusion,which can infer finer channel features,and the spatial attention can extract key information in space.The experiment results show that the improved model is better than the Squeeze and Excitation Module(SE),Convolutional Block Attention Module(CBAM)and Efficient Channel Attention Module(ECA).Compared with the 3D residual model without attention mechanism,the F1 score is improved by 1.03 percentage points and the accuracy is improved by 1.06 percentage points.Finally,the influence of channel attention and spatial attention on the model are analyzed through ablation experiments.

关 键 词：转炉炼钢 视频分类 三维卷积神经网络 残差网络 注意力机制 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]