AI-Driven Real-Time Monitoring of Ground-Nesting Birds: A Case Study on Curlew Detection Using YOLOv10

• URL: http://arxiv.org/abs/2411.15263v1
• Date: Fri, 22 Nov 2024 10:36:29 GMT
• Title: AI-Driven Real-Time Monitoring of Ground-Nesting Birds: A Case Study on Curlew Detection Using YOLOv10
• Authors: Carl Chalmers, Paul Fergus, Serge Wich, Steven N Longmore, Naomi Davies Walsh, Lee Oliver, James Warrington, Julieanne Quinlan, Katie Appleby,
• Abstract summary: This study presents an AI-driven approach for real-time species detection, focusing on the curlew (Numenius arquata) A custom-trained YOLOv10 model was developed to detect and classify curlews and their chicks using 3/4G-enabled cameras linked to the Conservation AI platform. Across 11 nesting sites in Wales, the model achieved high performance, with a sensitivity of 90.56%, specificity of 100%, and F1-score of 95.05% for curlew detections.
• Score: 0.07255608805275862
• License:
• Abstract: Effective monitoring of wildlife is critical for assessing biodiversity and ecosystem health, as declines in key species often signal significant environmental changes. Birds, particularly ground-nesting species, serve as important ecological indicators due to their sensitivity to environmental pressures. Camera traps have become indispensable tools for monitoring nesting bird populations, enabling data collection across diverse habitats. However, the manual processing and analysis of such data are resource-intensive, often delaying the delivery of actionable conservation insights. This study presents an AI-driven approach for real-time species detection, focusing on the curlew (Numenius arquata), a ground-nesting bird experiencing significant population declines. A custom-trained YOLOv10 model was developed to detect and classify curlews and their chicks using 3/4G-enabled cameras linked to the Conservation AI platform. The system processes camera trap data in real-time, significantly enhancing monitoring efficiency. Across 11 nesting sites in Wales, the model achieved high performance, with a sensitivity of 90.56%, specificity of 100%, and F1-score of 95.05% for curlew detections, and a sensitivity of 92.35%, specificity of 100%, and F1-score of 96.03% for curlew chick detections. These results demonstrate the capability of AI-driven monitoring systems to deliver accurate, timely data for biodiversity assessments, facilitating early conservation interventions and advancing the use of technology in ecological research.

Related papers

• Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data [0.06819010383838325]
  Camera traps offer enormous new opportunities in ecological studies. Current automated image analysis methods often lack contextual richness needed to support impactful conservation outcomes. Here we present an integrated approach that combines deep learning-based vision and language models to improve ecological reporting using data from camera traps.
  arXiv  Detail & Related papers  (2024-11-21T15:28:52Z)
• BuckTales : A multi-UAV dataset for multi-object tracking and re-identification of wild antelopes [0.6267336085190178]
  BuckTales is the first large-scale UAV dataset designed to solve multi-object tracking and re-identification problem in wild animals. The MOT dataset includes over 1.2 million annotations including 680 tracks across 12 high-resolution (5.4K) videos. The Re-ID dataset includes 730 individuals captured with two UAVs simultaneously.
  arXiv  Detail & Related papers  (2024-11-11T11:55:14Z)
• AI-Powered Dynamic Fault Detection and Performance Assessment in Photovoltaic Systems [44.99833362998488]
  intermittent nature of photovoltaic (PV) solar energy leads to power losses of 10-70% and an average energy production decrease of 25%. Current fault detection strategies are costly and often yield unreliable results due to complex data signal profiles. This research presents a computational model using the PVlib library in Python, incorporating a dynamic loss quantification algorithm.
  arXiv  Detail & Related papers  (2024-08-19T23:52:06Z)
• A Self-Supervised Task for Fault Detection in Satellite Multivariate Time Series [45.31237646796715]
  This work proposes a novel approach leveraging Physics-Informed Real NVP neural networks, renowned for their ability to model complex and high-dimensional distributions. The experiments involve various configurations, including pre-training with self-supervision, multi-task learning, and standalone self-supervised training. Results indicate significant performance improvements across all settings.
  arXiv  Detail & Related papers  (2024-07-03T07:19:41Z)
• Enhancing Pollinator Conservation towards Agriculture 4.0: Monitoring of Bees through Object Recognition [0.6334523276812193]
  This article explores the use of Computer Vision and Object Recognition to autonomously track and report bee behaviour from images. A novel dataset of 9664 images containing bees is extracted from video streams and annotated with bounding boxes. The trained model is then packaged within an explainable AI interface, which converts detection events into timestamped reports and charts.
  arXiv  Detail & Related papers  (2024-05-24T10:45:24Z)
• SatBird: Bird Species Distribution Modeling with Remote Sensing and Citizen Science Data [68.2366021016172]
  We present SatBird, a satellite dataset of locations in the USA with labels derived from presence-absence observation data from the citizen science database eBird. We also provide a dataset in Kenya representing low-data regimes. We benchmark a set of baselines on our dataset, including SOTA models for remote sensing tasks.
  arXiv  Detail & Related papers  (2023-11-02T02:00:27Z)
• 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)
• Deep object detection for waterbird monitoring using aerial imagery [56.1262568293658]
  In this work, we present a deep learning pipeline that can be used to precisely detect, count, and monitor waterbirds using aerial imagery collected by a commercial drone. By utilizing convolutional neural network-based object detectors, we show that we can detect 16 classes of waterbird species that are commonly found in colonial nesting islands along the Texas coast.
  arXiv  Detail & Related papers  (2022-10-10T17:37:56Z)
• 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)
• Seeing biodiversity: perspectives in machine learning for wildlife conservation [49.15793025634011]
  We argue that machine learning can meet this analytic challenge to enhance our understanding, monitoring capacity, and conservation of wildlife species. In essence, by combining new machine learning approaches with ecological domain knowledge, animal ecologists can capitalize on the abundance of data generated by modern sensor technologies.
  arXiv  Detail & Related papers  (2021-10-25T13:40:36Z)
• 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)

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.