Improved YOLOv8-based defect detection for furniture artificial panels under complex texture backgrounds

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.