Precise slag addition monitoring in steel continuous casting is critical, yet harsh industrial environments make this task extremely challenging. This research proposes a novel deep learning framework by integrating BiFormer and coordinate-aware squeeze-and-excitation (CoordSE) modules into the YOLOv11 architecture. To efficiently extract features of small slag particles against complex molten steel backgrounds, the BiFormer component employs a dual-level routing attention strategy. Concurrently, the CoordSE module captures spatial and channel-wise feature dependencies by combining direction-aware feature aggregation with multi-branch fully connected layers. Evaluated on a custom dataset of 2847 high-resolution industrial images, the proposed BiFormer-CoordSEBlock-YOLOv11 model achieved 82.5 ± 0.2% precision, 69.1 ± 0.3% recall, and 80.6 ± 0.2% mAP@0.5. Comprehensive ablation studies confirm that the BiFormer and CoordSE modules improved the baseline mAP@0.5 by 23.4% and 12.3%, respectively. Operating at a real-time inference speed of 45.2 FPS on standard hardware, this model offers a highly competitive framework for metallurgical process monitoring. However, the current recall rate of 69.1% and the lack of physical validation on resource-constrained edge devices represent limitations that must be systematically addressed before full-scale industrial deployment.
Li et al. (Sun,) studied this question.
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