STE-YOLO: A Surface Defect Detection Algorithm for Steel Strips

Abstract

To accurately detect defects, we propose an enhanced model based on YOLOv8, named STE-YOLO. To address the aforementioned challenges, this paper adopts YOLOv8 as the improved model. The structure of this paper is as follows: We enhance the model’s feature extraction and small detail recognition by integrating GhostConv into partial convolutions. In order to address the attention bias of the model, we introduce a Bottleneck Transformer self-attention convolution layer that effectively improves localization box accuracy. For the problem of defect category mismatches, we exploit the C2f-LSKA attention mechanism in the model head to address this issue. The experimental results indicate that the improved model achieves a mean average precision (mAP) of 79.0%, compared to 65.8% for the original model, marking an improvement of 13.1%. STE-YOLO significantly increases the precision of detecting surface defects on strip steel.