Detection of Printing Defects on Wood-Derived Paper Products Using an Improved YOLOv8n

Paper-based printing materials originate from the wood-based value chain–wood–pulp–paper–printing—and their yield reflects the utilization efficiency of pulp and paper resources. In roll-to-roll printing production, small printing defects (e.g., missing prints, smudges, cracks) often cause rework and scrap, thereby increasing the con-sumption of wood-derived materials. To improve resource efficiency, this study pro-poses a lightweight, improved YOLOv8n model for real-time small-defect detection. The Efficient IoU (EIoU) loss is introduced in the bounding box regression stage to im-prove localization accuracy, and a Squeeze-and-Excitation (SE) channel attention mechanism is embedded in the feature fusion stage to strengthen feature representation for small printing defects. Evaluations conducted on datasets collected from real pro-duction lines demonstrate that, with 3.02 M parameters and 8.1 GFLOPs, the model achieves mAP@0.5 = 94.1%, Precision = 95.1%, Recall = 94.3%, and an inference speed of 100.2 FPS, outperforming the baseline model. The proposed method contributes to re-ducing rework and material waste, supporting the efficient utilization of wood re-sources and the sustainable development of the paper-based packaging industry.