Pang, Y.; Yu, W.; Xuan, C.; Zhang, Y.; Wu, P. A Large Benchmark Dataset for Individual Sheep Face Recognition. Agriculture2023, 13, 1718.
Pang, Y.; Yu, W.; Xuan, C.; Zhang, Y.; Wu, P. A Large Benchmark Dataset for Individual Sheep Face Recognition. Agriculture 2023, 13, 1718.
Pang, Y.; Yu, W.; Xuan, C.; Zhang, Y.; Wu, P. A Large Benchmark Dataset for Individual Sheep Face Recognition. Agriculture2023, 13, 1718.
Pang, Y.; Yu, W.; Xuan, C.; Zhang, Y.; Wu, P. A Large Benchmark Dataset for Individual Sheep Face Recognition. Agriculture 2023, 13, 1718.
Abstract
The mutton sheep breeding industry has transformed significantly in recent years, from traditional grassland free-range farming to a more intelligent approach. As a result, automated sheep face recognition systems have become vital to modern breeding practices and have gradually replaced ear tagging and other manual tracking techniques. Although sheep face datasets have been introduced in previous studies, they have often involved pose or background restrictions (e.g., fixing of the subject’s head, cleaning of the face), which restrict data collection and have limited the size of available sample sets. As a result, a comprehensive benchmark designed exclusively for the evaluation of individual sheep recognition algorithms is lacking. To address this issue, this study develops a large-scale benchmark dataset, Sheepface-107, comprised of 5,350 images acquired from 107 different subjects. Images were collected from each sheep at multiple angles, including front and back views, in a diverse collection that provides a more comprehensive representation of facial features. In addition to the dataset, an assessment protocol is developed by applying multiple evaluation metrics to the results produced by three different deep learning models: VGG16, GoogLeNet, and ResNet50, which achieved F1-scores of 83.79%, 89.11%, and 93.44%, respectively. A statistical analysis of each algorithm suggested that accuracy and the number of parameters were the most informative metrics for use in evaluating recognition performance.
Keywords
sheep face recognotion; large benchmark; deep learning; convolutional neural networks; dataset
Subject
Engineering, Architecture, Building and Construction
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
