A detection model for corner cracks of continuous casting strand based on deep learning
Xiaoliang Meng, Sen Luo, Weiling Wang, Miaoyong Zhu
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.