Forestry pest recognition and detection method based on pooling vision Transformer
A detection method and forestry technology, applied in neural learning methods, character and pattern recognition, instruments, etc., can solve the problems of reduced recognition accuracy and stability, difficulty in segmentation, limited recognition ability, etc., and achieve high classification and recognition accuracy. , the effect of high accuracy and low image data requirements
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[0027] Example 1 The present invention in the experimental results of forestry pest data set
[0028] The index that the present invention uses is, and following table is that experiment preliminary result compares:
[0029]
[0030] Time: The time it takes for the classifier to train every 20 Epoch, in seconds.
[0031]The method of the present invention is compared with the traditional convolutional neural network methods such as AlexNet, Vgg16, Resnet18, Densenet121 in the forestry pest data set, and also compared with the ViT (VisionTransformer) based on the attention mechanism, and the accuracy rate (Accuracy) is selected. , training time and parameters are used as evaluation indicators. The accuracy rate of the PiT method used in the present invention is 92.6%. The PiT model introduces a pooling operation on the basis of using the Transformer structure, fully considering the local feature information and global structural information of the image. Therefore, the PiT ...
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