TL-ResNet50-SE: An Attention-Enhanced Transfer Learning Model for Surface Defect Detection in Solid Wood Panels

Traditional surface defect detection methods for solid wood panels have gradually exposed many drawbacks, such as poor performance of the detection model, low detection efficiency, and the scarcity of defect detection models applied to wood, which are difficult to meet the increasing production demand. In order to address the above problems, this paper proposes a TL-ResNet50 (Transfer Learning, abbreviated as TL) method for surface defect detection of solid wood panels by integrating the attention mechanism. First, the TL-ResNet50 model is constructed by introducing transfer learning, which improves the feature extraction capability and detection accuracy of the model compared with the ResNet50 model. Secondly, the SE (Squeeze-and-Excitation) and CBAM (Convolutional Block Attention Module) modules were integrated, and through ablation experiments, it was found that the TL-ResNet50-SE model was able to effectively capture the local and global features of the wood surface, thus improving the defect detection accuracy. The accuracy of defect detection is improved. Finally, the performance of classical models ResNet50, VGG16, AlexNet, EfficientNetV2, and the improved TL-ResNet50 model is systematically evaluated by comparing and analyzing the performance of each model in the wood defect detection task. The detection accuracy of the proposed model is 88.2%, which significantly improves the accuracy of surface defect detection of solid wood panels compared with other models.