This paper presents the design of a hardware–software system for non-invasive automated monitoring of feeding behavior in livestock with biometric identification of individual animals. Neural network models for animal identification from images and individual recognition have been developed and trained. A solution is proposed to address the challenge of acquiring a sufficient number of personalized animal images for training the identification neural network. A transfer learning approach is introduced for pig identification, where the network is first trained on a large-scale dataset of more than three million human face images obtained from open sources and subsequently fine-tuned by training the upper layers on a significantly smaller dataset consisting of 5610 pig face images. Experimental results demonstrated the high effectiveness of the system: the Top-1 identification accuracy reached 95.1%, while the ROC AUC in open-set recognition tasks achieved 0.95. The processing time per frame on an NVIDIA RTX 4090 GPU was 1.4 ms (724 FPS).
Ivashchuk et al. (Wed,) studied this question.
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