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.

翻译：鸟类是监测生物多样性和栖息地健康的重要指示物种，在生态系统管理中发挥关键作用。鸟类种群数量下降可能导致种子传播、授粉及害虫防治等生态系统服务功能减弱。准确且长期监测鸟类以识别受关注物种，同时评估保护干预措施成效，对生态学家至关重要。然而，监测工作耗时、成本高昂，且难以在长时间跨度和显著空间尺度上持续管理。相机陷阱、声学监测仪和无人机等技术为非侵入性监测提供了方法。使用相机陷阱监测存在两大问题：a) 相机生成大量图像，导致数据及时处理和分析困难；b) 高比例误报阻碍数据加工分析与报告生成。本文提出一种解决方案，通过利用深度学习实现相机陷阱数据中鸟类物种的实时分类与误报自动去除。采用Faster-RCNN架构对图像进行实时分类，借助3/4G网络传输图像并在图形处理单元（GPU）上完成处理，为生态保护工作者提供关键检测指标，从而消除人工观测需求。我们的模型实现了88.79%的平均灵敏度、98.16%的特异度和96.71%的准确率，充分证明了深度学习在鸟类自动监测中的有效性。