Lizard Body Temperature Acquisition and Lizard Recognition Using Artificial Intelligence

Abstract

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.

Keywords: YOLO; artificial intelligence; body temperature acquisition; computer vision; lizards; object detection.

Figure 1

Architecture diagram of YOLOv5s.

Figure 2

Scenario: (a) setup and (b) cardboard box used.

Figure 3

Example of a labelled dataset image in Roboflow.

Figure 4

The scenario used to obtain thermal images and their associated RGB images.

Figure 5

RGB camera output (left side) and thermal camera output (right side).

Figure 6

Detection of the lizard and its six body parts on the ROI (left).

Figure 7

All pixels within the (a) “Lizard” and (b) “Tail” bounding boxes are white.

Figure 8

Isolation of the ROI defined by the (a) “Lizard” and (b) “Tail” bounding boxes.

Figure 9

The (a) “Lizard” and (b) “Tail” bounding boxes are in grayscale.

Figure 10

The (a) “Lizard” and (b) “Tail” bounding boxes are in black and white (binary).

Figure 11

Bounding boxes and their respective central pixels are represented by a blue circle (in the ROI).

Figure 12

The initial pixel (centre) represents the “Lizard” (blue rectangle) and “Tail” (yellow rectangle) bounding boxes, marked with a blue dot. The final pixel representative of each bounding box is marked with a green dot.

Figure 13

The “src” parameter is represented by the coordinates of points 1, 2, 3, and 4, and the “dst” parameter is represented by the coordinates of points 1′, 2′, 3′, and 4′.

Figure 14

Bounding boxes and their representative pixels marked with blue dots (RGB image) and corresponding pixels marked with red dots in the thermal image.

Figure 15

Annotation of the maximum Y (Y_max), minimum Y (Y_min), and median X (X_med) relative to the coordinate axis of the input image.

Figure 16

Resulting graphs after training using a batch size of 32 and 500 epochs.

Figure 17

F1–confidence curve.

Figure 18

Precision–recall curve.

Figure 19

Example of an image from the “test set” with predictions.

Figure 20

Example of an image from the “test set” with noise and predictions.

Figure 21

Notepad with date, hour, class, and temperature values obtained: (a) from an image and (b) from a video.