In panel furniture production, furniture artificial panels are prone to defects, while manual inspection remains subjective and inconsistent. Automated non-destructive testing provides objective detection but faces challenges from severe feature distortion in complex texture backgrounds, reducing accuracy. This study proposes improved YOLOv8-based defect detection for furniture artificial panels under complex texture backgrounds. Initially, the C2f-MFM module is introduced to enhance defect–background separation through multi-dimensional feature mixing, effectively distinguishing edge defects. Subsequently, the MFS-Net module improves the detection of defects with diverse morphologies and random distributions across large IoU ranges. Ultimately, Wise-IoU v3 is employed to mitigate the impact of low-quality samples during training. Compared with the original YOLOv8s, the proposed model reduces parameters by 21.27% and FLOPs by 17.71%. Experiments on a self-constructed dataset demonstrate improvements of 1.9% in mAP@0.5 and 2.84% in mAP50–95, achieving 97.5% and 63.32%, respectively. Compared with other defect detection methods, the proposed approach shows clear advantages. Heatmap visualization using Grad-CAM validates its effectiveness, offering an efficient and robust solution for non-destructive defect detection in complex texture backgrounds.
Yang et al. (Thu,) studied this question.