Real-Time Wildlife Detection and Alert System Using Deep Learning and IoT

Communities near forests frequently face threats from wild animals entering human settlements, causing property damage and loss of life. Traditional protection relies on manual observation, often resulting in delayed responses. This project presents an automated monitoring and alert system integrating deep learning–based object detection with IoT hardware. Using YOLOv8, the system provides real-time recognition with a dual-model strategy—one general animal detector and a dedicated tiger model—to reduce false positives. Live video streams are continuously analyzed, and confirmed detections are stored in SQLite. On detection, alerts are triggered instantly via a web dashboard, buzzer, and SMS through a GSM module. Experimental evaluation demonstrated an average response time of about 2.3 seconds and an F1-score of 91%, showing the system is accurate and fast enough for deployment in high-risk areas.