# A Complete Pipeline to Extract Temperature from Thermal Images of Pigs

## TL;DR

This paper presents an automated system using thermal imaging and AI to accurately extract temperatures from specific regions on pigs.

## Contribution

A novel end-to-end pipeline combining thermal imaging and deep learning for automated temperature extraction in pigs is introduced.

## Key findings

- The segmentation model achieved 92.3% mean pixel class accuracy and 87.1% mean intersection over union.
- The system's extracted temperatures matched manual observations perfectly.
- The pipeline reliably operates without human intervention for temperature measurement in pigs.

## Abstract

Using deep learning or artificial intelligence (AI) in research with animals is a new interdisciplinary area of research. In this study, we have explored the potential of thermal imaging and AI in pig research. Thermal cameras play a vital role in obtaining and collecting a large amount of data, and AI has the capabilities of processing and extracting valuable information from these data. The amount of data collected using thermal imaging is huge, and automation techniques are therefore crucial to find a meaningful interpretation of the changes in temperature. In this paper, we present a complete pipeline to extract temperature automatically from a selected Region of Interest (ROI). This system consists of three stages: the first one checks whether the ROI is completely visible to observe the thermal temperature, and then the second stage uses an encoder–decoder structure of a convolution neural network to segment the ROI, if the condition was met at stage one. In the last stage, the maximum temperature is extracted and saved in an external file. The segmentation model showed good performance, with a mean Pixel Class accuracy of 92.3%, and a mean Intersection over Union of 87.1%. The extracted temperature observed by the model entirely matched the manually observed temperature. The system showed reliable results to be used independently without human intervention to determine the temperature in the selected ROI in pigs.