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Computer Vision for Wildlife Monitoring: Detecting Brown Howler Monkeys using YOLO

Summary

This study applies the YOLOv10 computer vision framework to automatically detect brown howler monkeys in camera trap videos, aiming to monitor canopy bridge usage and reduce the time conservationists spend reviewing false-positive images.

SourcearXiv Computer VisionAuthor: Gabriel Ferri Schneider, Guido Luis Glufke Mainardi, Paulo Ricardo Knob, Patr\'icia Dias, M\'arcia Jardim, J\'ulio C\'esar Bicca-Marques, Soraia Raupp Musse
Computer Vision for Wildlife Monitoring: Detecting Brown Howler Monkeys using YOLO
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[Submitted on 1 Jul 2026]

Title:Computer Vision for Wildlife Monitoring: Detecting Brown Howler Monkeys using YOLO

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Abstract:Urban expansion threatens global biodiversity, especially affecting arboreal species due to the fragmentation of forest habitats. The movement of arboreal species across disjointed forest patches increases mortality risk and, thus, compromises their conservation. In this context, the installation of canopy bridges can be a viable strategy; yet continuous monitoring of their use by arboreal species is essential for ensuring their effectiveness, typically carried out with the aid of camera traps. However, this method often produces false-positive images that demand time from conservationists for review. In this context, computer vision algorithms can optimize the task of detecting target species using the canopy bridges. In this study, we explored the automatic detection of brown howler monkeys (Alouatta guariba) in videos obtained by camera traps. Given the need for a large number of annotated images of the target animals to train the algorithms, we tested the incorporation of auxiliary data to improve detection models, fine-tuning the YOLOv10 framework using varying proportions of them. The improvement of these automatic detection techniques contributes to conservation efforts, by providing automatic tools to monitor solutions that minimize the impact of human interference in animals habitats.

Comments: Accepted on International Conference on Computer Animation, Social Agents, and Extended Reality '26 (CASAXR 26)

Subjects:

Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR)

Cite as: arXiv:2607.01396 [cs.CV]

(or arXiv:2607.01396v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2607.01396

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Gabriel Ferri Schneider [view email] [v1] Wed, 1 Jul 2026 18:57:09 UTC (9,008 KB)

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Key points and analysis

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Key points

  • Urban expansion fragments forests, endangering arboreal species; canopy bridges can help but need monitoring.
  • Camera traps often produce numerous false-positive images that require manual review, which is time-consuming.
  • Researchers fine-tuned YOLOv10 with auxiliary data to improve automatic detection accuracy of brown howler monkeys.
  • The approach provides automated tools to monitor conservation measures, helping mitigate human impact on animal habitats.

Highlights and analysis are generated automatically and may contain errors. Check the original source.

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