Cow face detection and recognition method based on deep learning

Abstract

The invention provides a cow face detection and recognition method based on deep learning. The method comprises the steps of bovine face data acquisition and preprocessing, bovine face data conversion, bovine face feature extraction model construction and bovine face feature vector database construction, bovine face detection model construction, bovine face part detection and feature extraction byusing the constructed bovine face feature extraction model and bovine face detection model, and bovine face recognition by using an image retrieval technology. According to the method, the data volume required by model training can be effectively reduced, the cattle face part is accurately drawn, the influence of environmental factors of the non-cattle face part on the recognition accuracy is reduced, multiple cattle face feature values are comprehensively used for cattle face recognition, and the cattle face recognition accuracy is improved.

Description

technical field [0001] The present invention relates to cow face detection and recognition technology, in particular to a cow face detection and recognition method based on deep learning. Background technique [0002] With the development of the technological era, more and more high-tech is being applied to the development of digital agriculture, including Internet of Things technology, remote sensing technology, artificial intelligence technology and so on. High-tech has achieved good results in crop production increase, pest protection, animal husbandry and other aspects. In addition to high-tech, the insurance industry has also made a non-negligible contribution to the development of digital agriculture. As a policy insurance that benefits agriculture, rural areas and farmers, dairy cow insurance can effectively improve the ability of farmers to deal with risks and reduce losses caused by disasters. However, the frequent occurrence of "insurance fraud" incidents has ser...

AI Technical Summary

Problems solved by technology

[0004] Because the prior art method requires a large amount of data for model training, and adopts the box to outline the part of the cow's face for identification, this will lead to two deficiencies: one is that the huge amount of data increases the burden of data collection, and greatly Increase the time spent on training the model; second, the box that outlines the cow's face part not only contains the cow's face part, but also includes background factors in the image. As the complexity of the environment increases, the recognition accuracy will also be greatly affected

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