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

2026-07-01

AI Summary

Urban expansion has led to habitat fragmentation for arboreal species, while conventional manual review of camera trap imagery remains inefficient and prone to false positives. To address this, this study presents the first application of YOLOv10 for automated detection of brown howler monkeys, fine-tuning the model using a combination of camera trap videos and auxiliary image data. The approach systematically evaluates the added value of auxiliary data in few-shot wildlife identification scenarios. Results demonstrate that the proposed method significantly improves detection accuracy and computational efficiency, substantially reducing the burden of manual annotation. Furthermore, it enables effective automated monitoring of canopy corridor use, thereby providing a robust technical foundation for evaluating the efficacy of conservation interventions.

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.

Problem

• wildlife monitoring
• arboreal species
• camera traps
• false positives
• brown howler monkeys

Innovation

• YOLOv10
• computer vision
• wildlife monitoring
• auxiliary data
• canopy bridges