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

• URL: http://arxiv.org/abs/2607.01396v1
• Date: Wed, 01 Jul 2026 18:57:09 GMT
• Title: Computer Vision for Wildlife Monitoring: Detecting Brown Howler Monkeys using YOLO
• Abstract summary: Urban expansion threatens global biodiversity, especially affecting arboreal species.<n>In this study, we explore the automatic detection of brown howler monkeys (Alouatta guariba) in videos obtained by camera traps.
• Score: 0.1759008116536278
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Re-Identifying Kākā with AI-Automated Video Key Frame Extraction [0.0]
  This study presents a unique pipeline for extracting high-quality key frames from videos of k=ak=a (Nestor meridionalis)<n>Using video recordings at a custom-built feeder, we extract key frames and evaluate the re-identification performance of our pipeline.<n>Results indicate that our proposed key frame selection methods yield image collections which achieve high accuracy in k=ak=a re-identification.
  arXiv  Detail & Related papers  (2025-10-09T19:46:46Z)
• Self-supervised Learning on Camera Trap Footage Yields a Strong Universal Face Embedder [48.03572115000886]
  This study introduces a fully self-supervised approach to learning robust chimpanzee face embeddings from unlabeled camera-trap footage.<n>We train Vision Transformers on automatically mined face crops, eliminating the need for identity labels.<n>This work underscores the potential of self-supervised learning in biodiversity monitoring and paves the way for scalable, non-invasive population studies.
  arXiv  Detail & Related papers  (2025-07-14T17:59:59Z)
• Automated Detection of Salvin's Albatrosses: Improving Deep Learning Tools for Aerial Wildlife Surveys [4.936287307711449]
  Unmanned Aerial Vehicles (UAVs) provide a cost-effective means of capturing high-resolution imagery.<n>We assess the performance of a general-purpose avian detection model, BirdDetector, in estimating the breeding population of Salvin's albatross (Thalassarche salvini) on the Bounty Islands, New Zealand.
  arXiv  Detail & Related papers  (2025-05-15T22:42:44Z)
• Improving Generalization Performance of YOLOv8 for Camera Trap Object Detection [0.0]
  This thesis explores the enhancements made to the YOLOv8 object detection algorithm to address the problem of generalization.<n>The proposed enhancements not only address the challenges inherent in camera trap datasets but also pave the way for broader applicability in real-world conservation scenarios.
  arXiv  Detail & Related papers  (2024-12-18T02:00:53Z)
• Multimodal Foundation Models for Zero-shot Animal Species Recognition in Camera Trap Images [57.96659470133514]
  Motion-activated camera traps constitute an efficient tool for tracking and monitoring wildlife populations across the globe. Supervised learning techniques have been successfully deployed to analyze such imagery, however training such techniques requires annotations from experts. Reducing the reliance on costly labelled data has immense potential in developing large-scale wildlife tracking solutions with markedly less human labor.
  arXiv  Detail & Related papers  (2023-11-02T08:32:00Z)
• Removing Human Bottlenecks in Bird Classification Using Camera Trap Images and Deep Learning [0.14746127876003345]
  Monitoring bird populations is essential for ecologists. Technology such as camera traps, acoustic monitors and drones provide methods for non-invasive monitoring. There are two main problems with using camera traps for monitoring: a) cameras generate many images, making it difficult to process and analyse the data in a timely manner. In this paper, we outline an approach for overcoming these issues by utilising deep learning for real-time classi-fication of bird species.
  arXiv  Detail & Related papers  (2023-05-03T13:04:39Z)
• Evaluation of the potential of Near Infrared Hyperspectral Imaging for monitoring the invasive brown marmorated stink bug [53.682955739083056]
  The brown marmorated stink bug (BMSB), Halyomorpha halys, is an invasive insect pest of global importance that damages several crops. The present study consists in a preliminary evaluation at the laboratory level of Near Infrared Hyperspectral Imaging (NIR-HSI) as a possible technology to detect BMSB specimens.
  arXiv  Detail & Related papers  (2023-01-19T11:37:20Z)
• Neuroevolution-based Classifiers for Deforestation Detection in Tropical Forests [62.997667081978825]
  Millions of hectares of tropical forests are lost every year due to deforestation or degradation. Monitoring and deforestation detection programs are in use, in addition to public policies for the prevention and punishment of criminals. This paper proposes the use of pattern classifiers based on neuroevolution technique (NEAT) in tropical forest deforestation detection tasks.
  arXiv  Detail & Related papers  (2022-08-23T16:04:12Z)
• Florida Wildlife Camera Trap Dataset [48.99466876948454]
  We introduce a challenging wildlife camera trap classification dataset collected from two different locations in Southwestern Florida. The dataset consists of 104,495 images featuring visually similar species, varying illumination conditions, skewed class distribution, and including samples of endangered species.
  arXiv  Detail & Related papers  (2021-06-23T18:53:15Z)
• Unifying data for fine-grained visual species classification [15.14767769034929]
  We present an initial deep convolutional neural network model, trained on 2.9M images across 465 fine-grained species. The long-term goal is to enable scientists to make conservation recommendations from near real-time analysis of species abundance and population health.
  arXiv  Detail & Related papers  (2020-09-24T01:04:18Z)
• Automatic image-based identification and biomass estimation of invertebrates [70.08255822611812]
  Time-consuming sorting and identification of taxa pose strong limitations on how many insect samples can be processed. We propose to replace the standard manual approach of human expert-based sorting and identification with an automatic image-based technology. We use state-of-the-art Resnet-50 and InceptionV3 CNNs for the classification task.
  arXiv  Detail & Related papers  (2020-02-05T21:38:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.