The strip running deviation in steel production can cause significant economic losses by forcing a shutdown of the whole steel production line. However, due to the fast running speed (100–140 m/min) of the strip, it a difficult problem to accurately judge online whether the strip running deviation or not and control its deviation during operation. In this paper, a fast and accurate model for detecting strip running deviation is proposed, this model allows for real-time control of strip operation deviation according to the detection model’s results. In our model, the attention module is used to improve the detection accuracy. The rolling equipment’s pressing force can be real-time controlled to correct the strip running deviation. Compared with the original model, the proposed model in this paper achieves an increase in accuracy of 3 %, and the detection speed can reach 29 FPS, meeting the real-time requirements. This work can provide ideas for applying computer vision in construction of intelligent factories.

钢铁生产中带钢跑偏会导致整条钢铁生产线停产，造成重大经济损失。但由于带钢运行速度较快（100~140 m/min），运行过程中在线准确判断带钢是否跑偏并控制跑偏是一个难题。本文提出了一种快速、准确的带钢运行偏差检测模型，该模型可以根据检测模型的结果实时控制带钢运行偏差。在我们的模型中，注意力模块用于提高检测精度。轧制设备的压紧力可实时控制，以纠正带钢跑偏。与原始模型相比，本文提出的模型精度提高了3%，检测速度可达29 FPS，满足实时性要求。该工作可为计算机视觉在智能工厂建设中的应用提供思路。