Steel surface defect detection based on improved YOLOv8

Aiming at the low efficiency of manual inspection and poor performance of traditional algorithms, an improved YOLOv8 based detection algorithm for surface defects of rolled steel was proposed. For YOLO v8 algorithm, An EMA attention module has been appended to the concluding layer of the backbone network, and the original CIoU was replaced with WIoU loss function. In view of the poor quality of some samples in the dataset, Wise-IoU V1 was selected, which avoided the problem of excessive punishment of low-quality samples by the original loss function to the greatest extent. The average accuracy is 80.4% and the recall rate is 74.7%. Compared to the original network, the average accuracy is improved by 4%. Finally, the paper discusses the attempt of extreme lightweight of the model, reducing the size of the model with a slight loss of mAP. It is possible to deploy the model on low performance devices.