Department of Biosystems and Biomaterials Science and Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea.
National Institute of Agricultural Sciences, Rural Development Administration, Jeollabuk-do 54875, Korea.
Sensors (Basel). 2019 Apr 6;19(7):1651. doi: 10.3390/s19071651.
Wild birds are monitored with the important objectives of identifying their habitats and estimating the size of their populations. Especially in the case of migratory bird, they are significantly recorded during specific periods of time to forecast any possible spread of animal disease such as avian influenza. This study led to the construction of deep-learning-based object-detection models with the aid of aerial photographs collected by an unmanned aerial vehicle (UAV). The dataset containing the aerial photographs includes diverse images of birds in various bird habitats and in the vicinity of lakes and on farmland. In addition, aerial images of bird decoys are captured to achieve various bird patterns and more accurate bird information. Bird detection models such as Faster Region-based Convolutional Neural Network (R-CNN), Region-based Fully Convolutional Network (R-FCN), Single Shot MultiBox Detector (SSD), Retinanet, and You Only Look Once (YOLO) were created and the performance of all models was estimated by comparing their computing speed and average precision. The test results show Faster R-CNN to be the most accurate and YOLO to be the fastest among the models. The combined results demonstrate that the use of deep-learning-based detection methods in combination with UAV aerial imagery is fairly suitable for bird detection in various environments.
野生鸟类的监测具有识别其栖息地和估计其种群规模的重要目标。特别是在候鸟的情况下，它们会在特定时期内被大量记录，以预测动物疾病（如禽流感）的可能传播。本研究在无人机（UAV）采集的航空照片的帮助下，构建了基于深度学习的目标检测模型。包含航空照片的数据集包括各种鸟类栖息地、湖泊附近和农田中的鸟类的不同图像。此外，还捕获了鸟类诱饵的航空图像，以实现各种鸟类图案和更准确的鸟类信息。创建了 Faster Region-based Convolutional Neural Network (R-CNN)、Region-based Fully Convolutional Network (R-FCN)、Single Shot MultiBox Detector (SSD)、Retinanet 和 You Only Look Once (YOLO) 等鸟类检测模型，并通过比较计算速度和平均精度来评估所有模型的性能。测试结果表明，在这些模型中，Faster R-CNN 的准确性最高，而 YOLO 的速度最快。综合结果表明，将基于深度学习的检测方法与无人机航空图像相结合，非常适合在各种环境中进行鸟类检测。
