YOLOv8-Driven Approach for Wildlife Detection and Recognition

Abstract

Wildlife monitoring is essential for biodiversity con- servation, agricultural protection, and environmental stability. Conventional surveillance methods often face challenges such as inefficiency, limited coverage, and delays in detection. To address these limitations, this paper proposes an advanced wildlife de- tection and recognition system utilizing YOLOv8, a state-of-the- art deep learning model known for its superior accuracy and rapid inference capabilities. The system is designed to effectively identify various animal species in both image and video data by leveraging YOLOv8’s enhanced architecture, which improves detection precision and adaptability in complex environments. The model demonstrates robust performance across diverse conditions, including varying illumination, environmental noise, and dynamic 
backgrounds. Experimental evaluation highlights the system’s high detection accuracy and efficient processing capabilities, making it suitable for deployment in agricultural zones, forested regions, and protected areas. This scalable and automated approach offers a promising solution for enhancing wildlife monitoring efforts and supporting conservation initiatives.