Research on Wood Defect Detection Algorithms Based on Improved Neural

To address the challenges of complex texture interference and the difficulty of detecting small-scale defects on wood surfaces, this study proposes an improved neural network-based detection algorithm, YOLOv11-RABRA. First, a Single Class Improve (SI) strategy is employed to selectively augment minority defect categories, thereby alleviating class imbalance and enhancing the model’s learning capability for rare defects. On this basis, a C3K2_RepVGG module is designed by integrating RepVGG re-parameterization, which optimizes the feature extraction and processing pipeline and strengthens defect feature representation. Furthermore, an ADown adaptive lightweight down-sampling mechanism is introduced to effectively preserve fine-grained features such as cracks, achieving a balance between accuracy and computational efficiency. Finally, a C2BRA dual-route attention module is applied to enhance defect-focused feature aggregation while suppressing interference from complex wood textures. Experimental results demonstrate that, compared with the baseline YOLOv11 model, the proposed algorithm achieves improvements of 13.7 percentage points in mAP50 and 12.8 percentage points in precision, significantly enhancing the accuracy and robustness of wood surface defect detection. In summary, the proposed method achieves advances in both detection performance and lightweight design, providing a feasible solution for high-precision detection in intelligent manufacturing and industrial quality inspection.