Diptera insect identification method based on deep convolutional neural network

Abstract

The invention provides a diptera insect recognition method based on a deep convolutional neural network, and the method comprises the following steps: collecting a diptera insect image, and making a data set of the diptera insect image; performing data enhancement on the data set; constructing an improved RetinaNet target detection model; setting training parameters, and training the RetinaNet target detection model through the data set; and classifying and positioning the test set images of the diptera insects based on the trained target detection model. According to the diptera insect identification method based on a deep convolutional neural network, the RetinaNet target detection model is adopted, meanwhile, an improved convolution block attention module is added, a feature pyramid network is improved, a large amount of manpower and material resources do not need to be consumed, the problem of dependence on manual design features can also be solved, and the image acquisition methodis easy to operate.

Description

technical field [0001] The invention relates to the field of insect species identification, in particular to a method for identifying Diptera insects based on a deep convolutional neural network. Background technique [0002] Diptera is the fourth largest order of Insecta after Coleoptera, Lepidoptera, and Hymenoptera. Diptera is a complete metamorphosis of insects with only one pair of wings. The body is generally short, wide or slender, cylindrical or spherical, and the body length rarely exceeds 25 mm. Diptera insects have a wide range of feeding habits, which can be roughly divided into herbivorous, scavenging or coprophagous, predatory and parasitic. Diptera insects are closely related to people's lives. Some of them transmit pathogens such as bacteria, parasites, and viruses between humans and animals, and also include the larvae of seed flies, leafminers, fruit flies, and wheat gall midges. important pests. Therefore, effectively identifying the species of Diptera ...

AI Technical Summary

Problems solved by technology

Deep learning algorithms in the field of target detection are mainly divided into two-stage target detection algorithms and single-stage target detection algorithms. The two-stage target detection algorithm has the problem of consuming detection time; the single-stage target detection algorithm has a fast detection speed, but the recognition has similar characteristics. The target accuracy rate of different features is low, especially for identifying Diptera insects with similar features such as shape, color and texture

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