AI Vision Framework For Wildlife Injury Detection And Rescue Alert In Dense Forest Environments

Authors: S. Nithyananth, SatheeshKumar S, Sathya Prakash. S, Sasivanan S, Sivaram S, Somiya M

Abstract: Illegal poaching, road accidents, and natural calamities in dense forests have led to the infliction of injuries on wildlife, which has resulted in extended periods of suffering and even death prior to any human intervention. Manual patrols are difficult, ineffective, and unsafe. In this paper, we propose an innovative AI Vision Framework for Wildlife Injury Detection and Rescue Alert (WIDRA) that leverages edge-based camera traps, unmanned aerial vehicles (UAVs), and deep learning algorithms to detect and classify injured animals in dense forests. The framework consists of three key components: (1) YOLOv8 for detection and classification of animals, (2) Temporal Convolutional Network (TCN) with attention mechanism to assess the level of injury through movement and postural analysis, and (3) LoRaWAN-based system for geotagged rescue notifications. Evaluated in simulated dense forest environments across two wildlife sanctuaries, the proposed system has attained 94.2% accuracy in animal detection, 89.6% sensitivity for injury classification, and shortened the rescue response time from 18 hours (with manual patrols) to 45 minutes.

DOI: https://doi.org/10.5281/zenodo.20415465