IJAEMS
IJAEMS
A real-time wildlife safety and hunter monitoring system that integrates AI-powered cameras with geofencing technology to enhance wildlife protection and prevent illegal hunting. In this research, a wild animal safety and hunter monitoring system that leverages advanced deep learning techniques of specifically YOLOv11 for object detection, and a deep Convolutional Neural Network of CNN for classification and feature refinement to enhance wildlife protection. The proposed system deploys AI-powered cameras across forest and buffer zones to automatically detect humans. The wild animals and hunting-related activities are shown in real time. YOLOv11 is utilised to perform fast and accurate detection of objects such as hunters, weapons, and animals under varied lighting and environmental conditions. The detected result as an output is further processed by a Deep CNN for fine-grained analysis. To ensure robust identification and reduced false alarms. The integration of geofencing allows the system to distinguish between legal hunting zones and restricted areas. To enable intelligent activity such as decision-making. When unauthorised activity is recognised, the system triggers immediate alerts to authorities through a centralised monitoring system. By combining the high-speed detection capabilities of YOLOv11 with the deep feature extraction power of CNN. This system provides a scalable, real-time time and reliable solution for wildlife conservation and antipoaching efforts. The experimental result demonstrates that the proposed system achieves an overall accuracy of 95.6% to confirming its reliability and effectiveness in monitoring wildlife and detecting illegal hunting activities.