Wild Animal Tracking for Effective Wildlife Conservation using YOLOv8 and Machine Learning Technologies

Wild animal tracking is crucial for conservation, especially for threatened species like tigers. The Amur tiger is critically endangered, with fewer than 600 left in the wild. The integration of machine learning and drone technology has revolutionized tiger tracking. Drones, or UAVs, play a vital role in wildlife conservation, while machine learning algorithms analyze complex data, make predictions and automate decision-making. This combination enables efficient processing of drone-generated data, including images, sounds and sensor readings.

This study utilized a dataset comprising 4,413 images sourced from Kaggle, originally derived from the ATRW (Annotated Tigers in the Wild) dataset. To ensure compatibility with the YOLOv8 model, the images were reformatted from PASCAL VOC annotation format to YOLO Darknet format. A series of preprocessing techniques were implemented, including image resizing, data augmentation (such as rotation, horizontal flipping and brightness adjustment) and pixel value normalization to enhance model generalization. The dataset was systematically partitioned into training, validation and test subsets to facilitate robust model evaluation and performance assessment.

The YOLOv8 model exhibited high efficacy in detecting tigers across diverse environmental conditions. It achieved a final Mean Average Precision (mAP) of 0.944, indicating robust detection accuracy. The model demonstrated strong generalization capabilities, effectively identifying tigers under varying illumination, occlusion and background complexities. These findings highlight the potential of YOLOv8 as a reliable tool for automated wildlife monitoring and conservation efforts.