An Automatic Recognition Approach for Tapping States Based on Object Detection

Monitoring tapping states, which reflects the smoothness of blast furnace (BF) production, is important in the blast furnace ironmaking process. Currently, these monitoring data are often recorded manually, which has limitations such as low reliability and high delays. In this study, we propose an automatic recognition approach for tapping states based on object detection, using furnace front monitoring videos combined with learning-based image processing technology. This approach addresses crucial aspects such as automatically recognizing the start and end times of iron tapping and slag discharging, accurately calculating their duration, and logging tapping sequences for multi-taphole operations. The experimental results demonstrate that this approach can meet the requirements of accurate and real-time recognition of tapping states and calculation of key monitoring data in industrial applications. The automatic recognition system developed based on this approach has been successfully applied in engineering projects, which provides real-time guidance for comprehensive monitoring, intelligent analysis, and operational optimization in blast furnace production.