Comprehensive Bird Preservation at Wind Farms

Wind as a clean and renewable energy source has been used by humans for centuries. However, in recent years with the increase in the number and size of wind turbines, their impact on avifauna has become worrisome. Researchers estimated that in the U.S. up to 500,000 birds die annually due to collisions with wind turbines. This article proposes a system for mitigating bird mortality around wind farms. The solution is based on a stereo-vision system embedded in distributed computing and IoT paradigms. After a bird’s detection in a defined zone, the decision-making system activates a collision avoidance routine composed of light and sound deterrents and the turbine stopping procedure. The development process applies a User-Driven Design approach along with the process of component selection and heuristic adjustment. This proposal includes a bird detection method and localization procedure. The bird identification is carried out using artificial intelligence algorithms. Validation tests with a fixed-wing drone and verifying observations by ornithologists proved the system’s desired reliability of detecting a bird with wingspan over 1.5 m from at least 300 m. Moreover, the suitability of the system to classify the size of the detected bird into one of three wingspan categories, small, medium and large, was confirmed.

[1]  Esteban Fernández-Juricic,et al.  Assessing bird avoidance of high-contrast lights using a choice test approach: implications for reducing human-induced avian mortality , 2018, PeerJ.

[2]  Rei Kawakami,et al.  Evaluation of Bird Detection using Time-lapse Images around a Wind Farm , 2015 .

[3]  K. Shawn Smallwood,et al.  Effects of Wind Turbine Curtailment on Bird and Bat Fatalities , 2020, The Journal of Wildlife Management.

[4]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[5]  Wlodek Kulesza,et al.  IoT-based information system for healthcare application : Design methodology approach , 2017 .

[6]  Hidde Leijnse,et al.  bioRad: biological analysis and visualization of weather radar data , 2018, Ecography.

[7]  Cris D. Hein,et al.  Behavior of bats at wind turbines , 2014, Proceedings of the National Academy of Sciences.

[8]  Santosh Bhusal,et al.  Improving Pest Bird Detection in a Vineyard Environment using Super-Resolution and Deep Learning , 2019 .

[9]  J. Kelly,et al.  The grand challenges of migration ecology that radar aeroecology can help answer , 2018, Ecography.

[10]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[11]  Vipul K. Dabhi,et al.  Voice Recognition and Voice Comparison using Machine Learning Techniques: A Survey , 2020, 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS).

[12]  Takeshi Naemura,et al.  Construction of a bird image dataset for ecological investigations , 2015, 2015 IEEE International Conference on Image Processing (ICIP).

[13]  Taber D. Allison,et al.  Automated monitoring for birds in flight: Proof of concept with eagles at a wind power facility , 2018, Biological Conservation.

[14]  Hidde Leijnse,et al.  Field validation of radar systems for monitoring bird migration , 2018, Journal of Applied Ecology.

[15]  Yi Xu,et al.  Object Detection in the Context of Mobile Augmented Reality , 2020, 2020 IEEE International Symposium on Mixed and Augmented Reality (ISMAR).

[16]  G. Triveni.  Bird Species Identification using Deep Fuzzy Neural Network , 2020 .

[17]  Dominique Chabot,et al.  Computer‐automated bird detection and counts in high‐resolution aerial images: a review , 2016 .

[18]  J. Thijssen,et al.  Mathematical Methods for Physics and Engineering: A Comprehensive Guide , 1998 .

[19]  Xaq Pitkow,et al.  Skip Connections Eliminate Singularities , 2017, ICLR.

[20]  Elizabeth A. Masden,et al.  Assessing vulnerability of marine bird populations to offshore wind farms. , 2013, Journal of environmental management.

[21]  Wolfgang Gründinger,et al.  The Renewable Energy Sources Act (EEG) , 2017 .

[22]  Rei Kawakami,et al.  BIRD DETECTION NEAR WIND TURBINES FROM HIGH-RESOLUTION VIDEO USING LSTM NETWORKS , 2016 .

[23]  P. Beasley,et al.  David Lack and the birth of radar ornithology , 2010 .

[24]  Xuyu Xiang,et al.  A biological image classification method based on improved CNN , 2020, Ecol. Informatics.

[25]  Shuying Li,et al.  CapsNet, CNN, FCN: Comparative Performance Evaluation for Image Classification , 2019 .

[26]  Jack B. Bishop,et al.  Review of international research literature regarding the effectiveness of auditory bird scaring techniques and potential alternatives. , 2003 .

[27]  Yo-Ping Huang,et al.  Bird Image Retrieval and Recognition Using a Deep Learning Platform , 2019, IEEE Access.

[28]  P. Gavali,et al.  Bird Species Identification using Deep Learning , 2019 .

[29]  Wlodek Kulesza,et al.  Depth reconstruction uncertainty analysis and improvement - The dithering approach , 2010, Image Vis. Comput..

[30]  Sandeep Singh Solanki,et al.  Automatic bird species recognition system using neural network based on spike , 2020 .

[31]  Yun Liu,et al.  Hand Gesture Recognition Based on HU Moments in Interaction of Virtual Reality , 2012, 2012 4th International Conference on Intelligent Human-Machine Systems and Cybernetics.

[32]  Peter P. Marra,et al.  Estimates of bird collision mortality at wind facilities in the contiguous United States , 2013 .

[33]  Ming Du,et al.  Computer vision algorithms and hardware implementations: A survey , 2019, Integr..

[34]  Mark Pearson,et al.  Using Neural Networks to Identify Bird Species from Birdsong Samples , 2020 .

[35]  Wlodek Kulesza,et al.  A Distributed Computing Real-Time Safety System of Collaborative Robot , 2020 .

[36]  Hermann Hötker,et al.  The impact of repowering of wind farms on birds and bats , 2006 .

[37]  Sang-Yeon Kim,et al.  Application of Deep-Learning Methods to Bird Detection Using Unmanned Aerial Vehicle Imagery , 2019, Sensors.

[38]  Adriaan M. Dokter,et al.  Biological Analysis and Visualization of Weather Radar Data [R package bioRad version 0.5.2] , 2020 .

[39]  Sergio A. Lambertucci,et al.  Human-wildlife conflicts in a crowded airspace , 2015, Science.

[40]  Óscar Muñoz-Jiménez,et al.  Estimates of aerial vertebrate mortality at wind farms in a bird migration corridor and bat diversity hotspot , 2020 .

[41]  Yasmin M. Kassim,et al.  Microvasculature segmentation of arterioles using deep CNN , 2017, 2017 IEEE International Conference on Image Processing (ICIP).

[42]  Megan S. Doppler,et al.  Cowbird responses to aircraft with lights tuned to their eyes: Implications for bird–aircraft collisions , 2015 .

[43]  Hans van Gasteren,et al.  Aeroecology meets aviation safety: early warning systems in Europe and the Middle East prevent collisions between birds and aircraft , 2019, Ecography.

[44]  S. L. Lima,et al.  Exploiting avian vision with aircraft lighting to reduce bird strikes , 2012 .

[45]  J. Mrovlje,et al.  Distance measuring based on stereoscopic pictures , 2008 .

[46]  R. Langston,et al.  Assessing the impacts of wind farms on birds , 2006 .

[47]  Marco Pierini,et al.  Is stereo vision a suitable remote sensing approach for motorcycle safety? An analysis of LIDAR, RADAR, and machine vision technologies subjected to the dynamics of a tilting vehicle. , 2018 .

[48]  Urmila Shrawankar,et al.  Deep Learning Neural Network for Identification of Bird Species , 2019 .