Smelting stage recognition for converter steelmaking based on the convolutional recurrent neural network

• A novel CRNN model integrating ResNet18 with CBAM and Bayesian optimization achieves 97.01% accuracy and 99.85% AUC for smelting stage recognition from furnace mouth flame videos. • The model outperforms traditional static image methods by effectively extracting spatiotemporal features, addressing dynamic changes in the smelting process. • Real-time performance is demonstrated with an average recognition time of 5.49 ms, making it suitable for industrial online monitoring. • The approach offers a non-contact, cost-effective alternative to sub-lance and mass spectrometry methods for converter endpoint control.