Deep Learning for Wildlife Monitoring: Near-Infrared Bat Detection Using YOLO Frameworks

Bats are ecologically vital mammals, serving as pollinators, seed dispersers, and bioindicators of ecosystem health. Many species inhabit natural caves, which offer optimal conditions for survival but present challenges for direct ecological monitoring due to their dark, complex, and inaccessible environments. Traditional monitoring methods, such as mist-netting, are invasive and limited in scope, highlighting the need for non-intrusive alternatives. In this work, we present a portable multisensor platform designed to operate in underground habitats. The system captures multimodal data, including near-infrared (NIR) imagery, ultrasonic audio, 3D structural data, and RGB video. Focusing on NIR imagery, we evaluate the effectiveness of the YOLO object detection framework for automated bat detection and counting. Experiments were conducted using a dataset of NIR images collected in natural shelters. Three YOLO variants (v10, v11, and v12) were trained and tested on this dataset. The models achieved high detection accuracy, with YOLO v12m reaching a mean average precision (mAP) of 0.981. These results demonstrate that combining NIR imaging with deep learning enables accurate and non-invasive monitoring of bats in challenging environments. The proposed approach offers a scalable tool for ecological research and conservation, supporting population assessment and behavioral studies without disturbing bat colonies.