Forestry pest recognition and detection method based on pooling vision Transformer

Abstract

The invention discloses a forestry pest recognition and detection method based on pooling vision Transform. The method aims at discriminating and detecting different types of forestry pests in different periods. The method mainly comprises the steps: reasonably dividing data sets of different periods and different types into a training set and a verification set; expanding training set pictures by using a sample enhancement technology; proposing a forestry pest recognition feature extraction method based on a pre-trained PiT neural network model, adopting an improved transfer learning method to perform secondary training on an initialized model by using a new data set on the basis of the trained model to obtain a forestry pest recognition classifier; and performing fine adjustment on model parameters by using the test set. By adopting the method, the types and periods of the forestry pests can be quickly and accurately recognized, and the requirement on the acquired image data is low.

Description

technical field [0001] The invention belongs to the field of forestry pest identification, and in particular relates to a method for identifying and detecting forestry pests based on pooled vision Transformer. Background technique [0002] Forestry pests in farmland pose a great threat to the normal growth of crops and seriously affect the high and stable yield of crops. At present, the traditional extensive large-scale chemical insecticide has produced many negative effects, such as environmental pollution and threats to food safety. It can reduce the impact on the field ecological environment, reduce economic costs, and improve the efficiency of insect removal. Therefore, combined with the current development trend of automated agriculture, it is particularly important to automatically and efficiently identify forestry pests. [0003] In recent years, with the rapid development of computer vision technology, it provides a technical basis for the automatic recognition and...

AI Technical Summary

Problems solved by technology

On the one hand, artificially designed image segmentation is difficult to obtain the feature expression closest to the natural attributes of the target, and the resulting recognition ability is limited

On the other hand, image preprocessing has an important impact on the recognition results, especially the segmentation of forestry pest eggs. For targets with a large degree of overlap, it is often difficult to segment or segment without affecting their respective feature expressions. , resulting in a decrease in the actual recognition accuracy and stability

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