Automatic Bird Identification for Offshore Wind Farms: A Case Study for Deep Learning

被引:0
|
作者
Niemi, Juha [1 ]
Tanttu, Juha T. [1 ]
机构
[1] Tampere Univ Technol, Signal Proc Lab, POB 300, Pori 28101, Finland
来源
PROCEEDINGS OF 2017 INTERNATIONAL SYMPOSIUM ELMAR | 2017年
关键词
Classification; Deep Learning; Convolutional Neural Networks; Machine Learning; Data Expansion; Wind Farms;
D O I
暂无
中图分类号
U6 [水路运输]; P75 [海洋工程];
学科分类号
0814 ; 081505 ; 0824 ; 082401 ;
摘要
An automatic bird identification system is required for offshore wind farms in Finland. Indubitably, a radar is the obvious choice to detect birds but actual identification requires external information such as digital images. The final bird species identification is based on a fusion of radar data and image data. We applied deep learning method for image classification and we developed a data expansion technique for the training data. We present classification results for the image classifier based on small convolutional neural network.
引用
收藏
页码:263 / 266
页数:4
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