基于混合金字塔模型的木材图像分类算法OA

In view of the problem of wood image cross-section classification,this study proposes a multi-token hybrid wood cross-section image classification algorithm called MixNet,which focuses on the accurate identification of information such as the geometric structure,statistical characteristics,texture,and edges of wood cross-sections.MixNet combines the local inductive bias capability of CNN(Convolutional Neural Networks)and the global context dependency capability of Transformer.It introduces dynamic convolution to adaptively adjust the size of the receptive field,capturing image features at different levels.A two-layer attention mechanism is incorporated to capture deep semantic information and global spatial information of images,and to learn multi-scale features at different positions across spaces,thereby improving the sensitivity to global information and further enhancing the recognition ability of the algorithm.Experimental results show that the highest recognition accuracy of MixNet in image classification tasks on the CIFAR100 and CIFAR10 public datasets is 72.32%and 93.64%respectively,which is 0.38 percentage points and 1.38 percentage points higher than that of TransNeXt.Although the accuracy of MixNet is lower than that of EfficientNet,MixNet has fewer parameters and lower computational complexity,resulting in lower computing costs.When applied to the Wood data dataset,the classification accuracy of MixNet reaches up to 99.92%.MixNet integrates the feature extraction advantages of CNN and Transformer,making it an effective wood classification algorithm.