Enhanced YOLOv9-Based Endangered Tiger Detection Framework for Wildlife Surveillance

Tiger conservation requires integrating diverse strategies, including preserving natural habitats, stringent anti-poaching efforts, and active community participation to ensure sustainable tiger population growth. The emergence of artificial intelligence presents an opportunity to enhance tiger surveillance through automated object detection. This paper introduces a robust, illumination-invariant framework that leverages Enlight-enGAN and the advanced YOLOv9c model for detecting Amur tigers. The fine-tuned YOLOv9c model, coupled with illumination enhancement techniques, achieves an impressive mAP50-95 score of 71.2% and an mAP50 score of 98.7% on the ATRW dataset. This approach significantly advances the state-of-the-art in tiger detection and highlights the potential of AI -driven solutions in wildlife conservation.