Slim-YOLOv8: A fast and accurate algorithm for surface defect detection of steel strips

Abstract

Steel strip is an extremely important industrial material and is widely used in various industrial fields. During the production process, surface defects need to be detected quickly and accurately. This study proposes a model. This study proposes a new slim-YOLOv8 (lightweight YOLOv8) detection model. The model is based on YOLOv8 and adopts a lightweight design paradigm, which reduces the number of parameters of the model and enhances the detection real-time performance. At the same time, an online reparameterization method is introduced to enhance the feature extraction capability of the network without raising the inference cost, and to improve the model's detection accuracy for complex defects. Finally, an auxiliary training head that can provide richer gradient information is added to the model to help train the model while preventing model overfitting. The performance of slim-YOLOv8 in mean average precision and parameters was evaluated on the well-known steel strip surface defect detection dataset NEU-DET, reaching a mAP of 85.8% at IoU 0.50 and 50.3% in the IoU 0.50–0.95 range. This is an improvement of 8.3% and 3%, respectively, compared to the baseline model. Meanwhile, the number of parameters of the model was reduced from 3.0 M to 2.7 M, which is 7% lower than the baseline model. The experimental results show that slim-YOLOv8 uses a smaller number of parameters, but has higher accuracy and is able to detect various defects in the dataset well.