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

arXiv: https://arxiv.org/abs/2305.02097 · HTML: https://mlsanity.com/paper/2305.02097/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 34 references that are in the map.
- First posted on arXiv 3 May 2023.

## Builds on (most cited first, 6 of 6)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [Efficient Pipeline for Camera Trap Image Review](https://mlsanity.com/paper/1907.06772/) (2019)
- [Bird Species Categorization Using Pose Normalized Deep Convolutional Nets](https://mlsanity.com/paper/1406.2952/) (2014)
- [Object Detection Networks on Convolutional Feature Maps](https://mlsanity.com/paper/1504.06066/) (2015)
- [Choosing an Appropriate Platform and Workflow for Processing Camera Trap Data using Artificial Intelligence](https://mlsanity.com/paper/2202.02283/) (2022)

## Built on by (most cited first, 4 of 4)

- [Harnessing Artificial Intelligence for Wildlife Conservation](https://mlsanity.com/paper/2409.10523/) (2024)
- [AI-Driven Real-Time Monitoring of Ground-Nesting Birds: A Case Study on Curlew Detection Using YOLOv10](https://mlsanity.com/paper/2411.15263/) (2024)
- [Autonomous AI Bird Feeder for Backyard Biodiversity Monitoring](https://mlsanity.com/paper/2508.09398/) (2025)
- [Democratising Camera Trap AI: An Open-Source Model for Detecting UK Mammals](https://mlsanity.com/paper/2606.10940/) (2026)

## Abstract (arXiv)

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.