A Novel Wood Surface Defect Detection Model Based on Improved YOLOv8

Abstract

To address the challenges posed by complex and variable backgrounds coupled with the small-target characteristics of wood surface defects such as knots and cracks, a novel wood surface defect detection model based on improved You Only Look Once version 8 (YOLOv8) is proposed. The model integrates a multi-head mixed self-attention mechanism into the backbone to improve the representation of fine-grained defect features. A learnable dynamic upsampling module replaces traditional nearest-neighbor interpolation to mitigate feature loss during resolution recovery. Additionally, a structural Re-parameterizable Block is adopted to enhance feature expressiveness during inference, and a small-object detection head is added to enhance the detection of small defects while minimizing both missed and incorrect detections. The experimental results demonstrate that the proposed model effectively enhances detection performance, increasing the mAP of the baseline model from 72.9% to 79.5%. Furthermore, the proposed model surpasses other YOLO variants in mAP across all defect categories. This improvement better meets the quality control requirements of wood processing and manufacturing, ensuring the quality of wood products.