Chinese Journal of Liquid Crystals and Displays, Volume. 36, Issue 5, 741(2021)

Visual detection of liquid leakage based on convolutional neural network

LI Si-han1, QIU Huai-li1、*, WU Jia2, and SHEN Yan2
Author Affiliations
  • 1[in Chinese]
  • 2[in Chinese]
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    Aiming at the problem of low accuracy of traditional image processing methods for liquid leakage detection in a factory with complex equipment structure, numerous types of debris, and severe ground wear, a leak detection algorithm based on CNN is proposed. The leak detection problems is analyzed, the data set is made, the VGG16 model is established. In order to avoid over-fitting state, combined with early stopping algorithm to train samples, the rapid and automatic detection of the leakage of complex pipelines is achieved. In industrial sites, this method can accurately identify the leakage and reduce the impact of noise interference. Finally, the superiority of this algorithm is verified by comparison with a variety of image processing methods. The results show that the test accuracy of the algorithm can reach 99.44%, and the prediction accuracy can reach 97.0%, which is higher than the accuracy of traditional image processing algorithms. The prediction time of a single picture is about 0.2 s, which can meet the detection needs of industrial sites.

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    LI Si-han, QIU Huai-li, WU Jia, SHEN Yan. Visual detection of liquid leakage based on convolutional neural network[J]. Chinese Journal of Liquid Crystals and Displays, 2021, 36(5): 741

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    Paper Information

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    Received: Nov. 26, 2020

    Accepted: --

    Published Online: Aug. 26, 2021

    The Author Email: QIU Huai-li (hlq@hfut.edu.cn)

    DOI:10.37188/cjlcd.2020-0314

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