Cattle face recognition method, operation control device, electronic equipment and storage medium

Abstract

The invention discloses a cattle face recognition method, which comprises the steps of collecting image data of cattle to obtain a cattle face data set, obtaining four feature extraction models according to the cattle face data set, a first feature extraction network and a second feature extraction network, and obtaining four groups of features according to the cattle face data set and the four feature extraction models; designing an integrated network to cooperate with the four feature extraction models to form a complete cattle face recognition network, inputting the four groups of features into the integrated network, and training the integrated network; performing cattle face recognition according to the cattle face recognition network and the cattle face data set; for cattle herds with limited individuals, the method can obtain high recognition accuracy and is high in applicability.

Description

technical field [0001] The invention relates to the field of image recognition, in particular to a cow face recognition method, an operation control device, an electronic device and a storage medium. Background technique [0002] At present, the identification of cattle mainly relies on ear tags, but it cannot be ruled out that the ear tags are artificially replaced. Therefore, manual comparison of historical photos is required, resulting in a series of problems, such as personnel management, identification difficulty, changes in physical signs, etc. There are two main types of algorithms for animal recognition. One is modified from traditional image feature extraction algorithms, which have the problems of large image angle restrictions, difficult algorithm modification, and poor applicability; the second is modified from deep learning classification algorithms to extract images. After the feature is matched with the database, there are problems of insufficient pertinence a...

AI Technical Summary

Problems solved by technology

[0002] At present, the identification of cattle mainly relies on ear tags, but it cannot be ruled out that the ear tags have been artificially replaced. Therefore, manual comparison of historical photos is required, resulting in a series of problems, such as personnel management, identification difficulty, and changes in physical signs. There are two main types of algorithms for animal recognition. One is transformed from the traditional image feature extraction algorithm, which has the problems of large image angle restrictions, difficult algorithm transformation, and poor applicability; the second is transformed from the deep learning classification algorithm, which extracts images. After the features are matched with the database, there are problems of insufficient pertinence and insufficient precision

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