Thermal-based Elephant Detection using YOLOv8 for Automated Wildlife Monitoring in Low-visibility Environments

Human-elephant conflict poses a significant threat to both wildlife conservation and rural livelihoods, particularly in regions bordering forest reserves. Traditional observation methods are often time-consuming, error-prone and limited under challenging environmental conditions, highlighting the need for automated detection systems.

This study employs the YOLOv8 deep learning framework for elephant detection in thermal imagery. A publicly available thermal elephant dataset from Roboflow was preprocessed to remove low-quality and corrupted images. The dataset included diverse elephant postures, distances and environmental conditions with YOLO-formatted bounding box annotations. YOLOv8 was fine-tuned via transfer learning, utilizing multi-scale detection to localize elephants accurately across varying sizes and thermal scenarios.

The model demonstrated stable convergence, with box, segmentation, classification and distribution focal losses decreasing consistently over 50 training epochs. Detection performance was high, achieving a precision of 0.964, recall of 0.903, mAP@50 of 0.937 and mAP@50-95 of 0.676. Qualitative evaluation confirmed accurate localization under low contrast, motion blur and occlusion. These results indicate that YOLOv8, combined with rigorous dataset preprocessing, provides reliable real-time elephant detection for forest surveillance and early warning systems.