Computer Applications and Software, Volume. 42, Issue 4, 57(2025)

BIRD DROPPINGS MONITORING SYSTEM FOR SMALL PHOTOVOLTAIC POWER STATION BASED ON MACHINE VISION

Wang Song, Gu Xiang, and Wang Qiang
Author Affiliations
  • School of Information Science and Technology, Nantong University, Nantong 226000, Jiangsu, China
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    References(6)

    [3] [3] Singla A, Singh K, Yadav V K. Environmental effects on performance of solar photovoltaic module[C]//Biennial International Conference on Power & Energy Systems: Towards Sustainable Energy, 2016: 978-983.

    [4] [4] Sisodia A K, Mathur R K. Impact of bird dropping deposition on solar photovoltaic module performance: A systematic study in western Rajasthan[J]. Environmental Science and Pollution Research, 2019, 26(12050): 31119-31132.

    [7] [7] Samara S, Natsheh E. Intelligent real-time photovoltaic panel monitoring system using artificial neural networks[J]. IEEE Access, 2019, 7(1): 50287-50299.

    [8] [8] Ali M U, Khan H F, Masud M, et al. A machine learning framework to identify the hotspot in photovoltaic module using infrared thermography[J]. Solar Energy, 2020, 208: 643-651.

    [11] [11] Zhang H, Zu K, Lu J, et al. EPSANet: An efficient pyramid split attention block on convolutional neural network[EB]. arXiv: 2105.14447, 2021.

    [13] [13] Stergiou A, Poppe R, Kalliatakis G. Refining activation downsampling with SoftPool[C]//2021 IEEE/CVF International Conference on Computer Vision (ICCV), 2021.

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    Wang Song, Gu Xiang, Wang Qiang. BIRD DROPPINGS MONITORING SYSTEM FOR SMALL PHOTOVOLTAIC POWER STATION BASED ON MACHINE VISION[J]. Computer Applications and Software, 2025, 42(4): 57

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    Paper Information

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    Received: Oct. 27, 2021

    Accepted: Aug. 25, 2025

    Published Online: Aug. 25, 2025

    The Author Email:

    DOI:10.3969/j.issn.1000-386x.2025.04.010

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