4 research outputs found
Visual identification of individual Holstein-Friesian cattle via deep metric learning
Holstein-Friesian cattle exhibit individually-characteristic black and white
coat patterns visually akin to those arising from Turing's reaction-diffusion
systems. This work takes advantage of these natural markings in order to
automate visual detection and biometric identification of individual
Holstein-Friesians via convolutional neural networks and deep metric learning
techniques. Existing approaches rely on markings, tags or wearables with a
variety of maintenance requirements, whereas we present a totally hands-off
method for the automated detection, localisation, and identification of
individual animals from overhead imaging in an open herd setting, i.e. where
new additions to the herd are identified without re-training. We propose the
use of SoftMax-based reciprocal triplet loss to address the identification
problem and evaluate the techniques in detail against fixed herd paradigms. We
find that deep metric learning systems show strong performance even when many
cattle unseen during system training are to be identified and re-identified -
achieving 98.2% accuracy when trained on just half of the population. This work
paves the way for facilitating the non-intrusive monitoring of cattle
applicable to precision farming and surveillance for automated productivity,
health and welfare monitoring, and to veterinary research such as behavioural
analysis, disease outbreak tracing, and more. Key parts of the source code,
network weights and underpinning datasets are available publicly.Comment: 37 pages, 14 figures, 2 tables; Submitted to Computers and
Electronics in Agriculture; Source code and network weights available at
https://github.com/CWOA/MetricLearningIdentification; OpenCows2020 dataset
available at https://doi.org/10.5523/bris.10m32xl88x2b61zlkkgz3fml1