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Automatic Bird Identification for Offshore Wind Farms

Research output: Chapter in Book/Report/Conference proceedingChapterScientificpeer-review

Standard

Automatic Bird Identification for Offshore Wind Farms. / Niemi, Juha; Tanttu, Juha.

Wind Energy and Wildlife Impacts : Balancing Energy Sustainability with Wildlife Conservation. ed. / Regina Bispo; Joana Bernardino; Helena Coelho; José Lino Costa. 1. ed. Springer, 2019.

Research output: Chapter in Book/Report/Conference proceedingChapterScientificpeer-review

Harvard

Niemi, J & Tanttu, J 2019, Automatic Bird Identification for Offshore Wind Farms. in R Bispo, J Bernardino, H Coelho & JL Costa (eds), Wind Energy and Wildlife Impacts : Balancing Energy Sustainability with Wildlife Conservation. 1 edn, Springer, Conference on Wind energy and Wildlife impacts, 2/05/11. https://doi.org/10.1007/978-3-030-05520-2

APA

Niemi, J., & Tanttu, J. (2019). Automatic Bird Identification for Offshore Wind Farms. In R. Bispo, J. Bernardino, H. Coelho, & J. L. Costa (Eds.), Wind Energy and Wildlife Impacts : Balancing Energy Sustainability with Wildlife Conservation (1 ed.). Springer. https://doi.org/10.1007/978-3-030-05520-2

Vancouver

Niemi J, Tanttu J. Automatic Bird Identification for Offshore Wind Farms. In Bispo R, Bernardino J, Coelho H, Costa JL, editors, Wind Energy and Wildlife Impacts : Balancing Energy Sustainability with Wildlife Conservation. 1 ed. Springer. 2019 https://doi.org/10.1007/978-3-030-05520-2

Author

Niemi, Juha ; Tanttu, Juha. / Automatic Bird Identification for Offshore Wind Farms. Wind Energy and Wildlife Impacts : Balancing Energy Sustainability with Wildlife Conservation. editor / Regina Bispo ; Joana Bernardino ; Helena Coelho ; José Lino Costa. 1. ed. Springer, 2019.

Bibtex - Download

@inbook{0c5e3de0c7894e9799ae067d43bcf012,
title = "Automatic Bird Identification for Offshore Wind Farms",
abstract = "There is a need for automatic bird identification system at offshore wind farms in Finland. The developed system should be able to operate from onshore, which is cost-effective in terms of installations and maintenance. Indubitably, a radar is the obvious choice to detect flying birds but external information is required for actual identification. A conceivable method is to exploit visual camera images. In the proposed system the radar detects birds and provides the coordinates to camera steering system. The camera steering system tracks the flying birds, thus enabling capturing a series of images. Classification is based on the images and it is implemented by a small convolutional neural network trained with a deep learning algorithm. 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 data provided by the radar and image data. We present the results of the number of correctly identified species based on manually taken images.",
keywords = "Image classification, Deep learning, Convolutional neural networks (CNNs), Machine learning, data augmentation",
author = "Juha Niemi and Juha Tanttu",
year = "2019",
month = "2",
doi = "10.1007/978-3-030-05520-2",
language = "English",
isbn = "978-3-030-05519-6",
editor = "Regina Bispo and Joana Bernardino and Helena Coelho and Costa, {Jos{\'e} Lino}",
booktitle = "Wind Energy and Wildlife Impacts",
publisher = "Springer",
edition = "1",

}

RIS (suitable for import to EndNote) - Download

TY - CHAP

T1 - Automatic Bird Identification for Offshore Wind Farms

AU - Niemi, Juha

AU - Tanttu, Juha

PY - 2019/2

Y1 - 2019/2

N2 - There is a need for automatic bird identification system at offshore wind farms in Finland. The developed system should be able to operate from onshore, which is cost-effective in terms of installations and maintenance. Indubitably, a radar is the obvious choice to detect flying birds but external information is required for actual identification. A conceivable method is to exploit visual camera images. In the proposed system the radar detects birds and provides the coordinates to camera steering system. The camera steering system tracks the flying birds, thus enabling capturing a series of images. Classification is based on the images and it is implemented by a small convolutional neural network trained with a deep learning algorithm. 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 data provided by the radar and image data. We present the results of the number of correctly identified species based on manually taken images.

AB - There is a need for automatic bird identification system at offshore wind farms in Finland. The developed system should be able to operate from onshore, which is cost-effective in terms of installations and maintenance. Indubitably, a radar is the obvious choice to detect flying birds but external information is required for actual identification. A conceivable method is to exploit visual camera images. In the proposed system the radar detects birds and provides the coordinates to camera steering system. The camera steering system tracks the flying birds, thus enabling capturing a series of images. Classification is based on the images and it is implemented by a small convolutional neural network trained with a deep learning algorithm. 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 data provided by the radar and image data. We present the results of the number of correctly identified species based on manually taken images.

KW - Image classification

KW - Deep learning

KW - Convolutional neural networks (CNNs)

KW - Machine learning

KW - data augmentation

U2 - 10.1007/978-3-030-05520-2

DO - 10.1007/978-3-030-05520-2

M3 - Chapter

SN - 978-3-030-05519-6

BT - Wind Energy and Wildlife Impacts

A2 - Bispo, Regina

A2 - Bernardino, Joana

A2 - Coelho, Helena

A2 - Costa, José Lino

PB - Springer

ER -