Abstract: 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.

Key words: converter steelmaking, video classification, 3D convolutional neural network, residual network, attention mechanism