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Article

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

by
José-Joel González-Barbosa
1,
Israel Cruz Rangel
1,
Alfonso Ramírez-Pedraza
1,2,
Raymundo Ramírez-Pedraza
3,
Isabel Bárcenas-Reyes
4,
Erick-Alejandro González-Barbosa
5 and
Miguel Razo-Razo
6,*
1
Instituto Politécnico Nacional, CICATA-Unidad Querétaro, Querétaro 76090, Mexico
2
Secretaría de Ciencia, Humanidades, Tecnología e Innovación SECIHTI, Ciudad de México 03940, Mexico
3
Facultad de Contaduria y Administración, Universidad Autónoma de Querétaro, Querétaro 76017, Mexico
4
Facultad de Ciencias Naturales, Universidad Autónoma de Querétaro, Querétaro 76230, Mexico
5
Tecnológico Nacional de México/ITS de Irapuato, Guanajuato 36821, Mexico
6
The University of Texas at Dallas, Richardson, TX 75080, USA
*
Author to whom correspondence should be addressed.
Signals 2025, 6(3), 46; https://doi.org/10.3390/signals6030046
Submission received: 25 July 2025 / Revised: 21 August 2025 / Accepted: 26 August 2025 / Published: 4 September 2025
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Abstract

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.
Keywords: near-infrared; bats detection; YOLO near-infrared; bats detection; YOLO

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MDPI and ACS Style

González-Barbosa, J.-J.; Rangel, I.C.; Ramírez-Pedraza, A.; Ramírez-Pedraza, R.; Bárcenas-Reyes, I.; González-Barbosa, E.-A.; Razo-Razo, M. Deep Learning for Wildlife Monitoring: Near-Infrared Bat Detection Using YOLO Frameworks. Signals 2025, 6, 46. https://doi.org/10.3390/signals6030046

AMA Style

González-Barbosa J-J, Rangel IC, Ramírez-Pedraza A, Ramírez-Pedraza R, Bárcenas-Reyes I, González-Barbosa E-A, Razo-Razo M. Deep Learning for Wildlife Monitoring: Near-Infrared Bat Detection Using YOLO Frameworks. Signals. 2025; 6(3):46. https://doi.org/10.3390/signals6030046

Chicago/Turabian Style

González-Barbosa, José-Joel, Israel Cruz Rangel, Alfonso Ramírez-Pedraza, Raymundo Ramírez-Pedraza, Isabel Bárcenas-Reyes, Erick-Alejandro González-Barbosa, and Miguel Razo-Razo. 2025. "Deep Learning for Wildlife Monitoring: Near-Infrared Bat Detection Using YOLO Frameworks" Signals 6, no. 3: 46. https://doi.org/10.3390/signals6030046

APA Style

González-Barbosa, J.-J., Rangel, I. C., Ramírez-Pedraza, A., Ramírez-Pedraza, R., Bárcenas-Reyes, I., González-Barbosa, E.-A., & Razo-Razo, M. (2025). Deep Learning for Wildlife Monitoring: Near-Infrared Bat Detection Using YOLO Frameworks. Signals, 6(3), 46. https://doi.org/10.3390/signals6030046

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