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

Jiajun Liu, Haokun Lin (), Yue Liu, Lei Xiong, Chenjing Li, Tinghu Zhou and Mike Ma
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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

Sustainability, 2023, vol. 15, issue 8, 1-18

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.

Keywords: oil pollution detection; attention mechanism; single scale retinex; global relation-aware (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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