Image recognition method based on VGG16 on insect taxonomy

Abstract

The invention relates to an image recognition method based on VGG16 on insect taxonomy. The method comprises the following steps: S1, establishing an image data set; S2, processing insect images of the image data set to obtain a training data set; S3, training the training data set by using a VGG16 model; S4, extracting a part of the image from the image data set as a reference image and a to-be-identified image, and performing corner detection to correct the reference image; S5, processing the image to be identified and the corrected reference image, inputting the processed image to be identified and the corrected reference image into the trained VGG16 model, and extracting image features; S6, visualizing the extracted image features to obtain a feature map; and S7, calculating the feature map image similarity SSIM of the to-be-identified image and all reference images under each type of insect mesh-level order, solving a mean value, and classifying the to-be-identified image to the type with the maximum mean value to serve as the mesh-level order to which the to-be-identified image belongs. According to the invention, the insect classification accuracy and efficiency are improved.

Description

technical field [0001] The invention belongs to the technical field of insect image recognition and classification, and in particular relates to an image recognition method based on VGG16 in insect taxonomy. Background technique [0002] In traditional insect classification methods, whether it is traditional taxonomy or numerical taxonomy, the main basis for classifying insects is the insect's physical characteristics, which include insect color, markings, body appendages (such as tumors, Points, cilia, etc.) and size (such as body length, body width) and so on. However, these physical characteristics cannot fully reflect the differences between the physical characteristics of different insect groups, and the classification of insects based on these physical characteristics cannot achieve a relatively high accuracy rate. [0003] With the rapid development and wide application of computer science, it is possible to use computer vision as a means to extract and analyze the f...

AI Technical Summary

Problems solved by technology

Before the emergence of deep learning convolutional neural network, the extraction of these features in pattern recognition mainly relied on manual extraction, which had a certain degree of subjectivity.

In addition, most of the existing image recognition methods based on deep learning do not pay attention to the results produced during the operation of the neural network.

The fully connected layer is destructive to the spatial structure of the image, which will bring loss to the recognition accuracy of the image to a certain extent

And at present, there are few studies based on convolutional neural network in insect taxonomy for image class recognition.

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