Optical Instruments, Volume. 45, Issue 2, 26(2023)

Fruit damage detection and classification based on attention mechanism

Jie ZHANG... Chunlei XIA*, Rongfu ZHANG, Julaiti HALIZHATI and Yi LIU |Show fewer author(s)
Author Affiliations
  • School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
  • show less
    References(12)

    [1] [1] MIAH M S, TASNUVA T, ISLAM M, et al. An advanced method of identification fresh rotten fruits using different convolutional neural wks[C]2021 12th International Conference on Computing Communication wking Technologies (ICCCNT). Kharagpur: IEEE, 2021: 1 − 7.

    [2] [2] KARAKAYA D, ULUCAN O, TURKAN M. A comparative analysis on fruit freshness classification[C]2019 Innovations in Intelligent Systems Applications Conference (ASYU). Izmir: IEEE, 2019: 1 4.

    [3] [3] WAJID A, SINGH N K, JUNJUN P, et al. Recognition of ripe, unripe scaled condition of ange citrus based on decision tree classification[C]2018 International Conference on Computing, Mathematics Engineering Technologies (iCoMET). Sukkur: IEEE, 2018: 1 4.

    [4] [4] SINGH S, SINGH N P. Machine learningbased classification of good rotten apple[M]KHARE A, TIWARY U, SETHI I, et al. Recent Trends in Communication, Computing, Electronics. Singape: Springer, 2019: 377 386.

    [5] [5] CHAKRABTY S, SHAMRAT F M J M, BILLAH M M, et al. Implementation of deep learning methods to identify rotten fruits[C]2021 5th International Conference on Trends in Electronics Infmatics (ICOEI). Tirunelveli: IEEE, 2021: 1207 1212.

    [7] [7] SELVARAJU R R, COGSWELL M, DAS A, et al. GradCAM: Visual explanations from deep wks via gradientbased localization[C]Proceedings of the 2017 IEEE International Conference on Computer Vision. Venice: IEEE, 2017: 618 626.

    [8] [8] HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning f image recognition[C]Proceedings of the 2016 IEEE Conference on Computer Vision Pattern Recognition. Las Vegas: IEEE, 2016: 770 778.

    [11] [11] HU J, SHEN L, SUN G. Squeezeexcitation wks[C]Proceedings of the 2018 IEEECVF Conference on Computer Vision Pattern Recognition. Salt Lake City: IEEE, 2018: 7132 7141.

    [13] [13] WOO S, PARK J, LEE J Y, et al. CBAM: Convolutional block attention module[C]Proceedings of the European Conference on Computer Vision (ECCV). Munich: Springer, 2018: 3 19.

    [17] [17] SIMONYAN K, ZISSERMAN A. Very deep convolutional wks f largescale image recognition[C]3rd International Conference on Learning Representations. San Diego: ICLR, 2015.

    [18] [18] SZEGEDY C, LIU W, JIA Y Q, et al. Going deeper with convolutions[C]Proceedings of the 2015 IEEE Conference on Computer Vision Pattern Recognition. Boston: IEEE, 2015: 1 9.

    [19] [19] SLER M, HOWARD A, ZHU M L, et al. MobileV2: inverted residuals linear bottlenecks[C]Proceedings of the 2018 IEEECVF Conference on Computer Vision Pattern Recognition. Salt Lake City: IEEE, 2018: 4510 4520.

    Tools

    Get Citation

    Copy Citation Text

    Jie ZHANG, Chunlei XIA, Rongfu ZHANG, Julaiti HALIZHATI, Yi LIU. Fruit damage detection and classification based on attention mechanism[J]. Optical Instruments, 2023, 45(2): 26

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Category: APPLICATION TECHNOLOGY

    Received: Sep. 17, 2022

    Accepted: --

    Published Online: Jun. 12, 2023

    The Author Email: XIA Chunlei (xiachunlei@usst.edu.cn)

    DOI:10.3969/j.issn.1005-5630.2023.002.004

    Topics