Wildlife conservation has become a critical global concern due to increasing incidents of illegal poaching and habitat destruction. Traditional monitoring methods are inefficient and resource-intensive, making it difficult to ensure continuous surveillance of vast wildlife reserves. This research proposes an AI-based Wildlife and Poaching Detection System using the YOLO (You Only Look Once) object detection model for real-time monitoring. The system integrates computer vision, deep learning, and web-based deployment to detect animals and unauthorized human activities from video feeds. The model is trained on diverse datasets and achieves high detection accuracy under varying environmental conditions. The system provides instant alerts, reduces dependency on manual patrolling, and enhances conservation efforts. The proposed solution is scalable, efficient, and suitable for deployment in real-world wildlife protection scenarios.
Saini et al. (Thu,) studied this question.