Chinese Journal of Liquid Crystals and Displays, Volume. 36, Issue 5, 741(2021)
Visual detection of liquid leakage based on convolutional neural network
[12] [12] GULCEHRE C, MOCZULSKI M, DENIL M, et al. Noisy activation functions[C]//Proceedings of the 33rd International Conference on Machine Learning. New York, USA: ACM, 2016.
[13] [13] SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[C]//Proceedings of the 3rd International Conference on Learning Representations. 2015.
[15] [15] BOUREAU Y L, BACH F, LECUN Y, et al. Learning mid-level features for recognition[C]//Proceedings of 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Francisco, CA: IEEE, 2010: 2559-2566.
[17] [17] PRECHELT L. Early stopping-but when?[M]//ORR G B, MLLER K R. Neural Networks: Tricks of the Trade. Berlin: IEEE, 1998.
[18] [18] YOO D, KWEON I S. Learning loss for active learning[C]//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach: IEEE, 2019: 93-102.
[20] [20] CANNY J. A computational approach to edge detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1986, PAMI-8(6): 679-698.
Get Citation
Copy Citation Text
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
Category:
Received: Nov. 26, 2020
Accepted: --
Published Online: Aug. 26, 2021
The Author Email: QIU Huai-li (hlq@hfut.edu.cn)
