Strip running deviation monitoring and feedback real-time in smart factories based on improved YOLOv5

Jun LUO, Gang WANG*, Mingliang ZHOU, Huayan PU

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

1 Citation (Scopus)


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.

Original languageEnglish
Article number100923
Number of pages8
JournalSustainable Computing: Informatics and Systems
Early online date13 Oct 2023
Publication statusPublished - Dec 2023
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2023 Elsevier Inc.


  • Attention module
  • Computer vision
  • Intelligent factories
  • Real-time control
  • Strip running deviation


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