WoodGLNet: a multi-scale network integrating global and local information for real-time classification of wood images

Current research on image classification has combined convolutional neural networks (CNNs) and transformers to introduce inductive biases to the model, enhancing its ability to handle long-range dependencies. However, these integrated models have limitations. Standard CNNs have a static nature, restricting their convolution from dynamically adjusting to input images, thus limiting feature expression capabilities. In addition, the static nature of CNNs impedes the seamless integration between features dynamically generated by self-attention mechanisms and static features generated by convolution when combined with transformers. Furthermore, during image processing, each model stage contains abundant information that cannot be fully utilized by single-scale convolution, ultimately impacting the network’s classification performance. To tackle these challenges, we propose WoodGLNet, a real-time multi-scale pyramid network that aggregates global and local information in an input-dependent manner and facilitates feature interaction through three scales of convolution. WoodGLNet utilizes efficient multi-scale global spatial decay attention modules and input-dependent multi-scale dynamic convolutions at different stages, enhancing the network’s inductive biases and expanding the effective receptive field. In CIFAR100 and CIFAR10 image classification tasks, WoodGLNet-T achieves Top-1 accuracies of 76.34% and 92.35%, respectively, outperforming EfficientNet-B3 by 1.03 and 0.86 percentage points. WoodGLNet-S and WoodGLNet-B attain Top-1 accuracies of 77.56%, 93.66%, and 80.12%, 94.27%, respectively. The experimental subjects of this study were sourced from the Shandong Province Construction Structural Material Specimen Museum, tasked with wood testing and requiring high real-time performance. To assess WoodGLNet’s real-time detection capabilities, 20 types of precious wood from the museum were identified in real time using the WoodGLNet network. The results indicated that WoodGLNet achieved a classification accuracy of up to 99.60%, with a recognition time of 0.013 s per single image. These findings demonstrate the network’s exceptional real-time classification and generalization abilities.