Lizard Body Temperature Acquisition and Lizard Recognition Using Artificial Intelligence

• Published in: Sensors (Basel, Switzerland) Switzerland), 2024-06, Vol.24 (13), p.4135
• Main Authors: Afonso, Ana L, Lopes, Gil, Ribeiro, A Fernando
• Format: Article
• Language: English
• Subjects: Algorithms; Animals; Artificial Intelligence; Automation; Biologists; Biology; Body temperature; Body Temperature - physiology; body temperature acquisition; Cameras; computer vision; Deep learning; Image Processing, Computer-Assisted - methods; Laboratories; lizards; Lizards - physiology; Machine learning; Neural networks; object detection; Video Recording - methods; Wildlife conservation; YOLO
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s24134135

The acquisition of the body temperature of animals kept in captivity in biology laboratories is crucial for several studies in the field of animal biology. Traditionally, the acquisition process was carried out manually, which does not guarantee much accuracy or consistency in the acquired data and was painful for the animal. The process was then switched to a semi-manual process using a thermal camera, but it still involved manually clicking on each part of the animal's body every 20 s of the video to obtain temperature values, making it a time-consuming, non-automatic, and difficult process. This project aims to automate this acquisition process through the automatic recognition of parts of a lizard's body, reading the temperature in these parts based on a video taken with two cameras simultaneously: an RGB camera and a thermal camera. The first camera detects the location of the lizard's various body parts using artificial intelligence techniques, and the second camera allows reading of the respective temperature of each part. Due to the lack of lizard datasets, either in the biology laboratory or online, a dataset had to be created from scratch, containing the identification of the lizard and six of its body parts. YOLOv5 was used to detect the lizard and its body parts in RGB images, achieving a precision of 90.00% and a recall of 98.80%. After initial calibration, the RGB and thermal camera images are properly localised, making it possible to know the lizard's position, even when the lizard is at the same temperature as its surrounding environment, through a coordinate conversion from the RGB image to the thermal image. The thermal image has a colour temperature scale with the respective maximum and minimum temperature values, which is used to read each pixel of the thermal image, thus allowing the correct temperature to be read in each part of the lizard.