SMART EYES: Deep Learning-Driven Animal Detection and Alerting System

Human encroachment into natural habitats has intensified human-wildlife conflicts, necessitating effective, real-time solutions to monitor animal activities and mitigate risks. This project, Smart Eyes, presents a cost-effective animal detection and alerting system utilizing the ATmega328P microcontroller, USB-to-TTL communication, and a buzzer. It integrates deep learning with hardware for robust performance in detecting and managing interactions with wildlife. The system employs the YOLOv5 (You Only Look Once, version 5) algorithm, renowned for its real-time object detection capabilities, to accurately identify animals such as elephants and cheetahs. Pretrained and fine-tuned on a comprehensive dataset, YOLOv5 ensures high precision by minimizing false detections—the ATmega328P processes detection signals, while the USB-to-TTL interface facilitates seamless communication with external devices. Alerts are generated via a buzzer, enabling prompt responses to wildlife intrusions. Designed for deployment in regions prone to human-wildlife conflict, this system reduces dependency on manual monitoring methods like camera traps or field observation. It enhances conservation efforts by providing real-time data, enabling proactive management, and safeguarding both human and animal populations. This project offers a scalable, efficient, and reliable solution for wildlife monitoring, addressing critical challenges in conservation and human-wildlife coexistence.