An Efficient Illumination Invariant Tiger Detection Framework for Wildlife Surveillance

TL;DR

This paper presents a novel illumination-invariant tiger detection framework using EnlightenGAN and YOLOv8, significantly improving detection accuracy in wildlife surveillance under varying lighting conditions.

Contribution

The paper introduces a new illumination-invariant detection framework combining EnlightenGAN and YOLOv8, enhancing tiger detection accuracy in challenging lighting environments.

Findings

YOLOv8 achieves 61% mAP without illumination enhancement.

Illumination enhancement increases mAP by 0.7%.

Framework improves state-of-the-art performance on ATRW dataset by 6-7%.

Abstract

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%.