Publication

Abstract : This project utilizes the YOLOv10 algorithm on the NVIDIA Jetson Nano board to create a real-time wild animal detection system. By continuously analyzing video feeds from wildlife monitoring setups, the system accurately and efficiently detects animals across diverse environmental conditions. Leveraging the Jetson Nano’s edge computing capabilities alongside YOLOv10’s advanced object detection, the research presents a cost-effective, low-power solution for automated wildlife monitoring. With its ability to continuously track and identify animals in remote areas, this approach enhances conservation efforts and provides valuable data for biodiversity studies and the protection of endangered species.