Summary of Ai-driven Real-time Monitoring Of Ground-nesting Birds: a Case Study on Curlew Detection Using Yolov10, by Carl Chalmers et al.
AI-Driven Real-Time Monitoring of Ground-Nesting Birds: A Case Study on Curlew Detection Using YOLOv10
by Carl Chalmers, Paul Fergus, Serge Wich, Steven N Longmore, Naomi Davies Walsh, Lee Oliver, James Warrington, Julieanne Quinlan, Katie Appleby
First submitted to arxiv on: 22 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel AI-driven approach has been developed 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 used 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, 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. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Wildlife monitoring is crucial for assessing biodiversity and ecosystem health. Birds like the curlew are important ecological indicators because they’re sensitive to environmental pressures. Camera traps help collect data, but processing it manually can be time-consuming. This study uses AI to detect species in real-time, focusing on curlews that are declining in population. The approach combines custom-trained models with cameras linked to a conservation platform. It works efficiently and accurately detects curlews and their chicks. |
Keywords
» Artificial intelligence » F1 score
