IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i8p6966-d1128835.html
   My bibliography  Save this article

Global Relation-Aware-Based Oil Detection Method for Water Surface of Catchment Wells in Hydropower Stations

Author

Listed:
  • Jiajun Liu

    (School of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China)

  • Haokun Lin

    (School of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China)

  • Yue Liu

    (School of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China)

  • Lei Xiong

    (School of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China)

  • Chenjing Li

    (School of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China)

  • Tinghu Zhou

    (Ankang Hydroelectric Power Station, State Grid Shaanxi Electric Power Company Limited, Ankang 725012, China)

  • Mike Ma

    (Ankang Hydroelectric Power Station, State Grid Shaanxi Electric Power Company Limited, Ankang 725012, China)

Abstract

The oil in hydropower station catchment wells is a source of water pollution which can cause the downstream river to become polluted. Timely detection of oil can effectively prevent the expansion of oil leakage and has important significance for protecting water sources. However, the poor environment and insufficient light on the water surface of catchment wells make oil pollution detection difficult, and the real-time performance is poor. To address these problems, this paper proposes a catchment well oil detection method based on the global relation-aware attention mechanism. By embedding the global relation-aware attention mechanism in the backbone network of Yolov5s, the main features of oil are highlighted and the minor information is suppressed at the spatial and channel levels, improving the detection accuracy. Additionally, to address the problem of partial loss of detail information in the dataset caused by the harsh environment of the catchment wells, such as dim light and limited area, single-scale retinex histogram equalization is used to improve the grayscale and contrast of the oil images, enhancing the details of the dataset images and suppressing the noise. The experimental results show that the accuracy of the proposed method achieves 94.1% and 89% in detecting engine oil and turbine oil pollution, respectively. Compared with the Yolov5s, Faster R-CNN, SSD, and FSSD detection algorithms, our method effectively reduces the problems of missing and false detection, and has certain reference significance for the detection of oil pollution on the water surface of catchment wells.

Suggested Citation

  • Jiajun Liu & Haokun Lin & Yue Liu & Lei Xiong & Chenjing Li & Tinghu Zhou & Mike Ma, 2023. "Global Relation-Aware-Based Oil Detection Method for Water Surface of Catchment Wells in Hydropower Stations," Sustainability, MDPI, vol. 15(8), pages 1-18, April.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:8:p:6966-:d:1128835
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/8/6966/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/8/6966/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gabriel A. González-Reyes & Susana Bayo-Besteiro & Jordi Vich Llobet & Juan A. Añel, 2020. "Environmental and Economic Constraints on the Use of Lubricant Oils for Wind and Hydropower Generation: The Case of NATURGY," Sustainability, MDPI, vol. 12(10), pages 1-15, May.
    2. Jie Sun & Yuquan Zhang & Bin Liu & Xinfeng Ge & Yuan Zheng & Emmanuel Fernandez-Rodriguez, 2022. "Research on Oil Mist Leakage of Bearing in Hydropower Station: A Review," Energies, MDPI, vol. 15(7), pages 1-24, April.
    3. Da Zhang & Shuailin Chen, 2021. "Insulator Contamination Grade Recognition Using the Deep Learning of Color Information of Images," Energies, MDPI, vol. 14(20), pages 1-15, October.
    4. Yan Chen & Zhilong Wang, 2022. "Marine Oil Spill Detection from SAR Images Based on Attention U-Net Model Using Polarimetric and Wind Speed Information," IJERPH, MDPI, vol. 19(19), pages 1-11, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Emanuele Quaranta, 2023. "Lubricant Oil Consumption and Opportunities for Oil-Free Turbines in the Hydropower Sector: A European Assessment," Energies, MDPI, vol. 16(2), pages 1-7, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:8:p:6966-:d:1128835. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.