International Research Journal of Education and Technology

his project introduces a robust YOLO-based framework designed to enhance the accuracy and efficiency of real-time animal detection using webcam feeds. By leveraging the strengths of the YOLO (You Only Look Once) algorithm, this model effectively captures both broad and detailed features of animals in dynamic environments,

creating a comprehensive detection tool. The architecture’s optimization strategies allow it to maintain high levels of detection accuracy, even in challenging conditions such as varying lighting and partial occlusions—common realities in wildlife monitoring. Experimental results demonstrate significant improvements in performance metrics, including precision, recall, and F1 score, when compared to traditional object detection approaches. The model's resilience, shown through evaluations on diverse datasets, indicates it is well-suited for applications in wildlife conservation, agriculture, and urban monitoring, where environmental conditions can fluctuate. Moreover, the computational efficiency of the framework supports its feasibility for real-time applications, providing a solution that is both accurate and resource-efficient. Through this work, we contribute a powerful tool to one of the pressing challenges in animal monitoring: achieving reliable detection in real-world scenarios. The success of this model underscores the potential of YOLO-based systems in advancing wildlife monitoring technologies, ultimately assisting researchers and conservationists with timely insights that can enhance decision-making and improve outcomes in animal management.

Key Words: Wildlife Conservation , YOLO , Deep Learning, Real-Time Monitoring , Computer Vision.