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

页码：263 / 266

页数：4

共 50 条

[41] A deep learning approach for automatic identification of ancient agricultural water harvesting systems
Tiwari, Arti
Silver, Micha
Karnieli, Arnon
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2023, 118
[42] Community benefits, framing and the social acceptance of offshore wind farms: An experimental study in England
Walker, Benjamin J. A.
Wiersma, Bouke
Bailey, Etienne
ENERGY RESEARCH & SOCIAL SCIENCE, 2014, 3 : 46 - 54
[43] Spare parts control strategies for offshore wind farms: A critical review and comparative study
Tusar, Md Imran Hasan
Sarker, Bhaba R.
WIND ENGINEERING, 2022, 46 (05) : 1629 - 1656
[44] Sequence-based modeling of deep learning with LSTM and GRU networks for structural damage detection of floating offshore wind turbine blades
Choe, Do-Eun
Kim, Hyoung-Chul
Kim, Moo-Hyun
RENEWABLE ENERGY, 2021, 174 : 218 - 235
[45] Study of Deep Learning and CMU Sphinx in Automatic Speech Recognition
Dhankar, Abhishek
2017 INTERNATIONAL CONFERENCE ON ADVANCES IN COMPUTING, COMMUNICATIONS AND INFORMATICS (ICACCI), 2017, : 2296 - 2301
[46] Forecasting Pitch Response of Floating Offshore Wind Turbines with a Deep Learning Model
Barooni, Mohammad
Sogut, Deniz Velioglu
CLEAN TECHNOLOGIES, 2024, 6 (02): : 418 - 431
[47] Study on automatic lithology identification based on convolutional neural network and deep transfer learning
Li, Shiliang
Dong, Yuelong
Zhang, Zhanrong
Lin, Chengyuan
Liu, Huaji
Wang, Yafei
Bian, Youyan
Xiong, Feng
Zhang, Guohua
DISCOVER APPLIED SCIENCES, 2024, 6 (06)
[48] Deep Transfer Learning-Based Automated Identification of Bird Song
Das, Nabanita
Padhy, Neelamadhab
Dey, Nilanjan
Bhattacharya, Sudipta
Tavares, Joao Manuel R. S.
INTERNATIONAL JOURNAL OF INTERACTIVE MULTIMEDIA AND ARTIFICIAL INTELLIGENCE, 2023, 8 (04): : 33 - 45
[49] Sites exploring prioritisation of offshore wind energy potential and mapping for wind farms installation: Iranian islands case studies
Nezhad, Meysam Majidi
Neshat, Mehdi
Piras, Giuseppe
Garcia, Davide Astiaso
RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2022, 168
[50] Intelligent Fault-Tolerant Active Power Control Using Reinforcement Learning for Offshore Wind Farms
Zhang, Xuanhe
Badihi, Hamed
Jadidi, Saeedreza
Yu, Ziquan
Zhang, Youmin
IEEE ACCESS, 2024, 12 : 83782 - 83795

← 1 2 3 4 5 →