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Removing Human Bottlenecks in Bird Classification Using Camera Trap Images and Deep Learning

Chalmers, C.; Fergus, P.; Wich, S.; Longmore, S.N.; Walsh, N.D.; Stephens, P.A.; Sutherland, C.; Matthews, N.; Mudde, J.; Nuseibeh, A.

Removing Human Bottlenecks in Bird Classification Using Camera Trap Images and Deep Learning Thumbnail


Authors

C. Chalmers

P. Fergus

S. Wich

S.N. Longmore

N.D. Walsh

C. Sutherland

N. Matthews

J. Mudde

A. Nuseibeh



Abstract

Birds are important indicators for monitoring both biodiversity and habitat health; they also play a crucial role in ecosystem management. Declines in bird populations can result in reduced ecosystem services, including seed dispersal, pollination and pest control. Accurate and long-term monitoring of birds to identify species of concern while measuring the success of conservation interventions is essential for ecologists. However, monitoring is time-consuming, costly and often difficult to manage over long durations and at meaningfully large spatial scales. 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; and (b) the high proportion of false positives hinders the processing and analysis for reporting. In this paper, we outline an approach for overcoming these issues by utilising deep learning for real-time classification of bird species and automated removal of false positives in camera trap data. Images are classified in real-time using a Faster-RCNN architecture. Images are transmitted over 3/4G cameras and processed using Graphical Processing Units (GPUs) to provide conservationists with key detection metrics, thereby removing the requirement for manual observations. Our models achieved an average sensitivity of 88.79%, a specificity of 98.16% and accuracy of 96.71%. This demonstrates the effectiveness of using deep learning for automatic bird monitoring.

Citation

Chalmers, C., Fergus, P., Wich, S., Longmore, S., Walsh, N., Stephens, P., …Nuseibeh, A. (2023). Removing Human Bottlenecks in Bird Classification Using Camera Trap Images and Deep Learning. Remote Sensing, 15(10), Article 2638. https://doi.org/10.3390/rs15102638

Journal Article Type Article
Acceptance Date May 15, 2023
Online Publication Date May 18, 2023
Publication Date May 2, 2023
Deposit Date May 15, 2023
Publicly Available Date May 15, 2023
Journal Remote Sensing
Electronic ISSN 2072-4292
Publisher MDPI
Peer Reviewed Peer Reviewed
Volume 15
Issue 10
Article Number 2638
DOI https://doi.org/10.3390/rs15102638
Publisher URL https://www.mdpi.com/journal/remotesensing

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