The rapid expansion of railway infrastructure in India has significantly improved transportation efficiency; however, it has also led to an increase in wildlife-train collisions (WTCs), resulting in serious ecological consequences. Railway tracks passing through forest and wildlife zones disrupt natural habitats, causing fragmentation and forcing animals into dangerous crossings. This threatens wildlife populations and creates operational challenges for railway systems, including train delays and infrastructure damage.

To address this issue, this project proposes an efficient wildlife monitoring system using computer vision and deep learning techniques. The system employs the YOLO (You Only Look Once) object detection model to identify humans and animal species such as elephants, lions, giraffes, zebras, and cheetahs in real-time. Integrated with Arduino-based hardware and camera modules, the system continuously monitors railway surroundings near forest areas. When animals or humans are detected near the tracks, alerts are sent to authorities through communication modules, enabling timely preventive action.

This system provides a proactive monitoring mechanism to reduce wildlife fatalities, improve railway safety, and support ecological conservation. It demonstrates the effective use of artificial intelligence and embedded systems in solving real-world environmental problems and promoting safer railway infrastructure.