A detection model for corner cracks of continuous casting strand based on deep learning

ABSTRACT

Continuous casting is a dominant process for steel production with high productivity, low cost and high automation, but it always suffers from corner defects in the strand. Thus, an in-situ and highly efficient detection of the strand corner crack is very urgent for high-quality steel production. In the present study, several models, namely, YOLOv5x, YOLOv5-S (YOLOv5 + ShuffleNet v2), YOLOv5-SF (YOLOv5 + ShuffleNet v2 + Focus) and YOLOv5-SFA (YOLOv5 + ShuffleNet v2 + Focus + Adam optimizer), are proposed. The experimental results show that among the four models, the mAP for YOLOv5-SFA increases fastest and the number of epochs for mAP reaches the maximum is least. The loss value is less than 0.01 and the training time is 0.369 h, which is reduced by 58.86% with the comparison of YOLOv5x. When only 100 images are used as training data, the detection accuracy is 99.64%, which increases 11.19% with comparison of YOLOv5x, and the detection time is only 0.021 s.

KEYWORDS:

• Continuously cast strand
• corner crack
• YOLOv5-SFA
• training time
• detection accuracy
• loss value
• small sample training
• detection time

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [grant number 52074076], [grant number 51804067], [grant number U1708259], [grant number U20A20272].