Cattle Face Recognition Using Deep Transfer Learning Techniques

被引：1

作者：

Ruchay, Alexey ^{[1
,2
]}

Akulshin, Ilya ^{[2
]}

Kolpakov, Vladimir ^{[1
,3
]}

Dzhulamanov, Kinispay ^{[1
]}

Guo, Hao ^{[4
]}

Pezzuolo, Andrea ^{[5
]}

机构：

[1] RAS, Fed Res Ctr Biol Syst & Agrotechnol, Orenburg, Russia

[2] Chelyabinsk State Univ, Chelyabinsk, Russia

[3] Orenburg State Univ, Orenburg, Russia

[4] China Agr Univ, Coll Land Sci & Technol, Beijing, Peoples R China

[5] Univ Padua, Dept Agron Food Nat Resources Anim & Environm, Dept Land Environm Agr & Forestry, Legnaro, Italy

来源：

PROCEEDINGS OF 2023 IEEE INTERNATIONAL WORKSHOP ON METROLOGY FOR AGRICULTURE AND FORESTRY, METROAGRIFOR | 2023年

基金：

俄罗斯科学基金会;

关键词：

Precision Livestock Farming; Animal face Recognition; Animal Face Identification; Livestock Housing; Artificial Intelligence; Deep Learning; IDENTIFICATION;

D O I：

10.1109/MetroAgriFor58484.2023.10424103

中图分类号：

S2 [农业工程];

学科分类号：

0828 ;

摘要：

In this research, a new approach has been proposed for cattle face recognition using RGB images based on deep convolutional neural network. Nowadays, biometric identification of animals is a major problem in computer vision and livestock sector. In this research, all RGB images were preprocessed to improve recognition reliability. A deep learning model was carried out using additional data augmentation methods and fine neural network tuning. The pre-trained neural networks chosen were VGGFACE and VGGFACE2. As a result, the VGGFACE2 pre-trained neural network was chosen to identify cattle faces with 97.1% accuracy.

引用

页码：569 / 574

页数：6

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