[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-82610-en":3,"doc-seo-82610-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},82610,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Computer Vision for Wildlife Monitoring: Detecting Brown Howler Monkeys using YOLO","Urban expansion threatens global biodiversity, especially arboreal species whose habitat fragmentation limits safe movement across forest remnants. Canopy bridges may mitigate this problem, but effectiveness depends on continuous monitoring, which camera traps struggle to scale due to large volumes of false-positive images and costly manual review. This study introduces an automated video-based approach to detect brown howler monkeys (Alouatta guariba) using camera-trap footage. YOLOv10 is fine-tuned with mixed real and auxiliary synthetic data in Unity to alleviate the data bottleneck.","Computer Vision for Wildlife Monitoring: Detecting Brown Howler Monkeys using YOLO  \narXiv :2607 .0 1396v 1 [ cs .CV] 1 Jul 2026  \nGabriel Ferri Schneider PUCRS  \ngabriel.ferri@edu.pucrs.br  \nPaulo Ricardo Knob PUCRS [paulo.knob@edu.pucrs.br](paulo.knob@edu.pucrs.br)  \nGuido Luis Glufke Mainardi PUCRS  \n[guido.mainardi@edu.pucrs.br](guido.mainardi@edu.pucrs.br)[ ](guido.mainardi@edu.pucrs.br)Patr´ıcia Dias UERGS [patricia-dias@uergs.edu.br](patricia-dias@uergs.edu.br)  \nMrcia Jardim SEMA  \n[marcia-jardim@sema.rs.gov.br](marcia-jardim@sema.rs.gov.br)  \nJ´ulio Csar Bicca-Marques PUCRS [jcbicca@pucrs.br](jcbicca@pucrs.br)  \nSoraia Raupp Musse PUCRS  \n[soraia.musse@pucrs.br](soraia.musse@pucrs.br)  \nAbstract  \nUrban 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  \nto 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.  \nKeywords: Computer Vision, YOLO, Synthetic Data, Automatic Detection  \n1 Introduction  \nHabitat fragmentation driven by urban expansion and deforestation poses a critical threat to global biodiversity, isolating wildlife in diminishing forest remnants [1, 2] . For arboreal species, the loss of canopy connectivity increases the risk of mortality from road accidentsand electrocution, while simultaneously restricting gene flow and access to resources [3, 4] . In this context, canopy bridges have emerged as a practical mitigation strategy, providing artificial  \npathways that facilitate safe movement across fragmented landscapes [5, 6, 7] .  \nThe evaluation of these conservation measures relies heavily on continuous monitoring, typically conducted via indirect observation and camera traps [8, 9, 10] . Although effective, camera traps produce an overwhelming volume of data, much of which consists of falsepositive triggers caused by vegetation movement or weather conditions. The manual analysis of thousands of hours of footage is not only time-consuming but also creates a significant delay in assessing conservation outcomes [11] . In this context, Convolutional Neural Networks (CNNs) have demonstrated significant potential in automating the detection and classification of primates [12, 13, 14] . However, the development of high-performance models is often hindered by the ”data bottleneck”, it means, the requirement for large, manually annotated datasets [15, 16] . Synthetic data generation through computer graphics (CG) offers a scalable alternative, allowing for the creation of diverse, pre-labeled training sets, that can supplement limited real-world data.  \nThis study explores a hybrid approach, integrating real-world footage with synthetic data generated in Unity 1 , to improve the detection of the brown howler monkey (Alouatta guariba) . We utilize the YOLOv10 [17] architecture to quantify the performance gains from data mixing and ","cbCaikQtItV5Em2I","https://ap.wps.com/l/cbCaikQtItV5Em2I","pdf",11099681,1,11,"English","en",105,"# Abstract\n# Introduction\n# Related Work","[{\"question\":\"Why is monitoring canopy bridges important for conserving arboreal species?\",\"answer\":\"Monitoring verifies whether arboreal species use canopy bridges as intended, which is essential because habitat fragmentation increases mortality risk and can compromise conservation outcomes.\"},{\"question\":\"What problem do camera traps create during wildlife monitoring?\",\"answer\":\"Camera traps generate overwhelming amounts of data with many false-positive triggers from vegetation movement or weather, making manual review time-consuming and delaying conservation assessment.\"},{\"question\":\"How does the study improve brown howler monkey detection?\",\"answer\":\"The approach fine-tunes the YOLOv10 framework using a hybrid of real camera-trap videos and auxiliary synthetic data generated in Unity, evaluating different proportions to reduce the need for large manually annotated 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is monitoring canopy bridges important for conserving arboreal species?","Question",{"text":74,"@type":75},"Monitoring verifies whether arboreal species use canopy bridges as intended, which is essential because habitat fragmentation increases mortality risk and can compromise conservation outcomes.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"What problem do camera traps create during wildlife monitoring?",{"text":79,"@type":75},"Camera traps generate overwhelming amounts of data with many false-positive triggers from vegetation movement or weather, making manual review time-consuming and delaying conservation assessment.",{"name":81,"@type":72,"acceptedAnswer":82},"How does the study improve brown howler monkey detection?",{"text":83,"@type":75},"The approach fine-tunes the YOLOv10 framework using a hybrid of real camera-trap videos and auxiliary synthetic data generated in Unity, evaluating different proportions to reduce the need for large manually annotated 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