Wood Defect Detection Algorithm Integrating Multi-Scale Features

This paper proposes a lightweight wood defect detection model, termed MADORE-Minify, which effectively addresses the limitations of existing methods in balancing multi-scale target representation and parameter redundancy in wood defect detection. The overall architecture of MADORE-Minify follows the YOLO11 framework and is further optimized and enhanced upon this baseline. First, the backbone network integrates the Mixed Local Channel Attention (MLCA) module to strengthen feature extraction capability, enabling more effective detection of small-scale defects while al-leviating semantic information loss for large-scale targets. Second, the ADown downsampling module is employed to replace the upsampling layers, enhancing the model’s ability to capture features at different scales, particularly in preserving fine details of small targets. Meanwhile, this design reduces the spatial resolution of feature maps, thereby significantly decreasing computational cost and parameter complexity. Finally, an OREPA-GENet module is designed, which transforms nonlinear blocks into linear ones and com-presses complex structures into a single convolutional layer during the inference stage, achieving substan-tial model lightweighting and efficiency improvement. Extensive experiments conducted on a public wood defect dataset demonstrate the superiority of the proposed MADORE-Minify algorithm. Compared with several competing methods, MADORE-Minify achieves a better balance between detection accuracy and model lightweighting. In addition, the proposed model attains high detection accuracy with only 6.6M parameters, validating its outstanding performance and lightweight characteristics in the field of wood defect detection.