An Efficient Illumination Invariant Tiger Detection Framework for Wildlife Surveillance
This work addresses automated tiger detection for conservation efforts, representing an incremental improvement in a domain-specific application.
The paper tackles tiger detection in wildlife surveillance by proposing an illumination-invariant framework combining EnlightenGAN and YOLOv8, achieving a 61% mAP score without enhancement and a 0.7% improvement with it, elevating state-of-the-art performance on the ATRW dataset by 6-7%.
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%.