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Appl. Sci. 2018, 8(11), 2089; https://doi.org/10.3390/app8112089

Deep Learning Case Study for Automatic Bird Identification

1,†,‡,* and 2,†
1
Signal Processing Laboratory, Tampere University of Technology, 28101 Pori, Finland
2
Mathematics Laboratory, Tampere University of Technology, 28101 Pori, Finland
This paper is an extended version of our paper published in 2017 International Symposium ELMAR
Current address: Tampere University of Technology, Signal Processing Laboratory, P.O. Box 300, 28101 Pori, Finland.
*
Author to whom correspondence should be addressed.
Received: 27 September 2018 / Revised: 22 October 2018 / Accepted: 23 October 2018 / Published: 29 October 2018
(This article belongs to the Special Issue Advanced Intelligent Imaging Technology)
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Abstract

An automatic bird identification system is required for offshore wind farms in Finland. Indubitably, a radar is the obvious choice to detect flying birds, but external information is required for actual identification. We applied visual camera images as external data. The proposed system for automatic bird identification consists of a radar, a motorized video head and a single-lens reflex camera with a telephoto lens. A convolutional neural network trained with a deep learning algorithm is applied to the image classification. We also propose a data augmentation method in which images are rotated and converted in accordance with the desired color temperatures. The final identification is based on a fusion of parameters provided by the radar and the predictions of the image classifier. The sensitivity of this proposed system, on a dataset containing 9312 manually taken original images resulting in 2.44 × 106 augmented data set, is 0.9463 as an image classifier. The area under receiver operating characteristic curve for two key bird species is 0.9993 (the White-tailed Eagle) and 0.9496 (The Lesser Black-backed Gull), respectively. We proposed a novel system for automatic bird identification as a real world application. We demonstrated that our data augmentation method is suitable for image classification problem and it significantly increases the performance of the classifier. View Full-Text
Keywords: machine learning; deep learning; convolutional neural networks; classification; data augmentation; intelligent surveillance systems machine learning; deep learning; convolutional neural networks; classification; data augmentation; intelligent surveillance systems
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Niemi, J.; Tanttu, J.T. Deep Learning Case Study for Automatic Bird Identification. Appl. Sci. 2018, 8, 2089.

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