Journal of Applied Optics, Volume. 45, Issue 6, 1204(2024)

Video smoke recognition based on random patch shift and deformable attention

Yehui XIE and Haitao ZHAO*
Author Affiliations
  • School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China
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    References(18)

    [1] C POPE, D DOCKERY. Health effects of fine particulate air pollution: lines that connect. Journal of the Air & Waste Management Association, 56, 707-708(2006).

    [2] Jinting SHI, Feiniu YUAN, Xue XIA. Video smoke detection: a literature survey. Journal of Image and Graphics, 23, 303-322(2018).

    [3] G MIRANDA, A LISBOA, D VIEIRA et al. Color feature selection for smoke detection in videos, 31-36(2014).

    [4] Y C HSU, T H HUANG, T Y HU et al. Project RISE: recognizing industrial smoke emissions. Proceedings of the AAAI Conference on Artificial Intelligence, 35, 14813-14821(2021).

    [5] Z YIN, B WAN, F YUAN et al. A deep normalization and convolutional neural network for image smoke detection. IEEE Access, 5, 18429-18438(2017).

    [6] L HE, X GONG, S ZHANG et al. Efficient attention based deep fusion CNN for smoke detection in fog environment. Neurocomputing, 434, 224-238(2021).

    [7] Y LIU, W QIN, K LIU et al. A dual convolution network using dark channel prior for image smoke classification. IEEE Access, 7, 60697-60706(2019).

    [8] Y ZHOU, J WANG, T HAN et al. Fire smoke detection based on vision transformer, 39-43(2022).

    [9] K SIMONYAN, A ZISSERMAN. Very deep convolutional networks for large-scale image recognition, 1-14(2015).

    [10] H TAO, M LU, Z HU et al. Attention-aggregated attribute-aware network with redundancy reduction convolution for video-based industrial smoke emission recognition. IEEE Transactions on Industrial Informatics, 18, 7653-7664(2022).

    [11] G LIN, Y ZHANG, G XU et al. Smoke detection on video sequences using 3D convolutional neural networks. Fire Technology, 55, 1827-1847(2019).

    [12] Y CAO, Q TANG, X WU et al. EFFNet: enhanced feature foreground network for video smoke source prediction and detection. IEEE Transactions on Circuits and Systems for Video Technology, 32, 1820-1833(2021).

    [13] H TAO, Q DUAN. An adaptive frame selection network with enhanced dilated convolution for video smoke recognition. Expert Systems with Applications, 215, 119371(2023).

    [14] A VASWANI, N SHAZEER, N PARMAR et al. Attention is all you need, 5998-6008(2017).

    [15] Z LIU, Y LIN, Y CAO et al. Swin transformer: hierarchical vision transformer using shifted windows, 10012-10022(2021).

    [16] Z XIA, X PAN, S SONG et al. Vision transformer with deformable attention, 4794-4803(2022).

    [17] Z LIU, J NING, Y CAO et al. Video swin transformer, 3202-3211(2022).

    [18] W XIANG, C LI, B WANG et al. Spatiotemporal self-attention modeling with temporal patch shift for action recognition, 627-644(2022).

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    Yehui XIE, Haitao ZHAO. Video smoke recognition based on random patch shift and deformable attention[J]. Journal of Applied Optics, 2024, 45(6): 1204

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    Paper Information

    Category:

    Received: Sep. 21, 2023

    Accepted: --

    Published Online: Jan. 14, 2025

    The Author Email: ZHAO Haitao (赵海涛)

    DOI:10.5768/JAO202445.0602005

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