Defect Detection Method of Carbon Fiber Unidirectional Band Prepreg Based on Enhanced YOLOv8s

Abstract

To address the challenges in existing carbon fiber prepreg surface defect detection processes, specifically difficulties in small target detection and inaccuracies in elongated defects with large aspect ratios, this study proposes an enhanced YOLOv8s-based models for defect detection on the surface of carbon fiber unidirectional band prepreg. The proposed model respectively integrates two attention mechanisms, a Global Attention Mechanism (GAM) and a Deformable Large Kernel Attention (DLKA) Mechanism, into the architecture of YOLOv8s, which is a lightweight version of YOLO optimized for speed. The attention mechanisms are inserted between the backbone network and the detection head, respectively, to enhance feature extraction before target localization. The YOLOv8s-GAM model achieves a mean average precision (mAP@0.5) of 84.4%, precision of 79.3%, and recall of 78.3%, while the YOLOv8s-DLKA model shows improved performance with mAP@0.5 of 86.4%, precision of 82.4%, and recall of 80.2%. Compared with the original YOLOv8s model, these two modified models demonstrate improvements in mAP@0.5 of 1.6% and 3.6%, precision gains of 0.9% and 4.1%, and recall enhancements of 1.4% and 3.6%, respectively. These models provide technical solutions for precise defect identification on the surface of carbon fiber unidirectional band prepreg.