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

Abstract

Background: 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. Methods: 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. Result: 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.