Technical note: ShinyAnimalCV: open-source cloud-based web application
  for object detection, segmentation, and three-dimensional visualization of
  animals using computer vision
Jin Wang Yu Hu Lirong Xiang Gota Morota Samantha A. Brooks Carissa L. Wickens Emily K. Miller-Cushon Haipeng Yu
Computer vision (CV), a non-intrusive and cost-effective technology, has
furthered the development of precision livestock farming by enabling optimized
decision-making through timely and individualized animal care. The availability
of affordable two- and three-dimensional camera sensors, combined with various
machine learning and deep learning algorithms, has provided a valuable
opportunity to improve livestock production systems. However, despite the
availability of various CV tools in the public domain, applying these tools to
animal data can be challenging, often requiring users to have programming and
data analysis skills, as well as access to computing resources. Moreover, the
rapid expansion of precision livestock farming is creating a growing need to
educate and train animal science students in CV. This presents educators with
the challenge of efficiently demonstrating the complex algorithms involved in
CV. Thus, the objective of this study was to develop ShinyAnimalCV, an
open-source cloud-based web application. This application provides a
user-friendly interface for performing CV tasks, including object segmentation,
detection, three-dimensional surface visualization, and extraction of two- and
three-dimensional morphological features. Nine pre-trained CV models using
top-view animal data are included in the application. ShinyAnimalCV has been
deployed online using cloud computing platforms. The source code of
ShinyAnimalCV is available on GitHub, along with detailed documentation on
training CV models using custom data and deploying ShinyAnimalCV locally to
allow users to fully leverage the capabilities of the application.
ShinyAnimalCV can contribute to CV research and teaching in the animal science
community.