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PIG FACE RECOGNITION MODEL BASED ON A CASCADED NETWORK

文献类型： 外文期刊

作者： Wang, Rong 1 ; Shi, ZaiFeng 3 ; Li, Qifeng 2 ; Gao, Ronghua 2 ; Zhao, Chunjiang 1 ; Feng, Lu 2 ;

作者机构： 1.Northwest Agr & Forestry Univ, Coll Informat Engn, Yangling, Shaanxi, Peoples R China

2.Beijing Res Ctr Informat Technol Agr, Beijing, Peoples R China

3.Tianjin Univ, Tianjin, Peoples R China

4.Natl Engn Res Ctr Informat Technol Agr, Beijing, Peoples R China

5.Key Lab Informat Technol Agr, Beijing, Peoples R China

关键词： CNN; Deep learning; Pig face detection; Pig face recognition

期刊名称：APPLIED ENGINEERING IN AGRICULTURE （ 影响因子：0.985； 五年影响因子：1.02 ）

ISSN： 0883-8542

年卷期： 2021 年 37 卷 5 期

页码：

收录情况： SCI

摘要： The identification and tracking of livestock using artificial intelligence technology have been a research hotspot in recent years. Automatic individual recognition is the key to realizing intelligent feeding. Although RFID can achieve identification tasks, it is expensive and easily fails. In this article, a pig face recognition model that cascades a pig face detection network and a pig face recognition network is proposed. First, the pig face detection network is utilized to crop the pig face images from videos and eliminate the complex background of the pig shed. Second, batch normalization, dropout, skip connection, and residual modules are exploited to design a pig face recognition network for individual identification. Finally, the cascaded network model based on the pig face detection and recognition network is deployed on a GPU server, and an application is developed to automatically recognize individual pigs. Additionally, class activation maps generated by grad-CAM are used to analyze the performance of features of pig faces learned by the model. Under free and unconstrained conditions, 46 pigs are selected to make a positive pig face dataset, original multiangle pig face dataset and enhanced multiangle pig face dataset to verify the pig face recognition cascaded model. The proposed cascaded model reaches accuracies of 99.38%, 98.96%, and 97.66% on the three datasets, which are higher than those of other pig face recognition models. The results of this study improved the recognition performance of pig faces under multiangle and multi-environment conditions.

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