  Birds are important indicators for monitoring both biodiversity and habitat
health; they also play a crucial role in ecosystem management. Decline in bird
populations can result in reduced eco-system 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 classi-fication 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 cam-eras and processed using Graphical Processing Units
(GPUs) to provide conservationists with key detection metrics therefore
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.
