InfraNet: An Ensemble Approach for Real-time Wildlife Detection using Infrared Thermal Imaging

Human-wildlife conflict presents significant challenges to both conservation and human safety, necessitating efficient monitoring systems for timely wildlife detection. We introduce InfraNet, an infrared object detection system designed for real-time wildlife monitoring using embedded devices. Our key contributions are (1) a new annotated infrared dataset of elephants, human, and common domestic animals, curated to capture diverse environmental conditions, and (2) an ensemble methodology that combines predictions of multiple preprocessed thermal image versions using a baseline YOLOv8m model without fine-tuning. Experimental results on a set of Elephant dataset show that the proposed ensemble approach significantly increases recall rate from 0.35 to 0.62. Additionally, the ensemble model achieves real-time inference speeds on an NVIDIA Jetson Xavier NX, making it suitable for field deployment.