Tiger conservation necessitates the strategic deployment of multifaceted initiatives encompassing the preservation of ecological habitats, anti-poaching measures, and community involvement for sustainable growth in the tiger population. With the advent of artificial intelligence, tiger surveillance can be automated using object detection. In this paper, an accurate illumination invariant framework is proposed based on EnlightenGAN and YOLOv8 for tiger detection. The fine-tuned YOLOv8 model achieves a mAP score of 61% without illumination enhancement. The illumination enhancement improves the mAP by 0.7%. The approaches elevate the state-of-the-art performance on the ATRW dataset by approximately 6% to 7%.

翻译：老虎保护需要战略性地部署多层面举措，包括生态栖息地保护、反偷猎措施以及社区参与，以促进老虎种群的可持续增长。随着人工智能的出现，老虎监测可通过目标检测实现自动化。本文基于EnlightenGAN和YOLOv8提出了一种精确的光照不变性框架用于老虎检测。经过微调的YOLOv8模型在无光照增强的情况下达到了61%的mAP分数。光照增强使mAP提升了0.7%。该方法在ATRW数据集上将现有最优性能提升了约6%至7%。