Open Access
Open access
Applied Sciences (Switzerland), volume 8, issue 11, pages 2089

Deep Learning Case Study for Automatic Bird Identification

Publication typeJournal Article
Publication date2018-10-29
scimago Q2
SJR0.508
CiteScore5.3
Impact factor2.5
ISSN20763417
Computer Science Applications
Process Chemistry and Technology
General Materials Science
Instrumentation
General Engineering
Fluid Flow and Transfer Processes
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.

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