A New Monitoring Method for the Injection Volume of Blast Furnace Clay Gun Based on Object Detection

Monitoring the injection volume of clay guns is important in blast furnace ironmaking. Currently, such monitoring data are often recorded manually, which has limitations such as low reliability and high delay. To address these issues, we revisit the task from a computer vision perspective and propose an object detection method. First, we introduce an interpolation annotation technique to build a clay gun dataset. With it as a foundation, an improved object detection model called Faster R-CNN with multi-stage Positional encoding and two-branch Self Challenge (PSCfrcn) is proposed. Our model leverages multi-stage positional encoding (PE) to focus more on the relative position of local features, and a self-challenge (SC) module to mitigate the interference caused by the harsh environments. We conduct extensive experiments on the clay gun dataset from actual industrial scenarios, and use various metrics to validate the performance. Experimental results demonstrate that our method can significantly enhance the model’s discriminative power and generalization ability, which is a promising direction for this task.