Wildlife Detection And Recognition Using YOLO V8

Abstract

The use of YOLOv8 for wildlife detection and recognition has transformed real-time monitoring across diverse environments, particularly in rural, forested, and human-wildlife conflict zones. Its lightweight architecture, efficient feature extraction, and deep learning capabilities make it a preferred tool for wildlife conservation. YOLOv8’s ability to detect and classify animals in real-time has enhanced wildlife population monitoring, reduced risks of human-wildlife encounters, and contributed to biodiversity conservation. A major advancement in YOLOv8 is its ability to perform well under low-visibility conditions, such as nighttime, fog, and haze. These scenarios traditionally present significant c hallenges for detection models, as poor lighting and environmental interference can obscure critical visual features. However, YOLOv8, along with enhanced models like YOLO-SAG and WL-YOLO, addresses these issues by incorporating attention mechanisms, adaptive preprocessing, and lightweight modules. This allows the models to maintain high detection accuracy, often exceeding 97. Nighttime detection has been significantly improved by integrating glow reduction and adaptive preprocessing techniques, which handle artificial lighting, light scattering, and low contrast—issues that typically hinder detection in nocturnal settings. As a result, YOLOv8 and similar models offer robust and accurate detection in dimly lit environments. These enhancements in YOLOv8-based models provide a balance between accuracy, speed, and computational efficiency, reducing false positives and increasing reliability in real-time applications. With its ability to handle low visibility and complex environments, YOLOv8 is a crucial tool for wildlife conservationists, supporting real-time monitoring, behavior analysis, and rapid response to human-wildlife conflicts