Chinese Journal of Ship Research, Volume. 19, Issue 5, 95(2024)

Ship crack detection method based on lightweight fast convolution and bidirectional weighted feature fusion network

Chong WANG1,2 and Yuhui ZHU1,2
Author Affiliations
  • 1Key Laboratory of High Performance Ship Technology of Ministry of Education, Wuhan University of Technology, Wuhan 430063, China
  • 2School of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology, Wuhan 430063, China
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    References(17)

    [2] [2] YANN L, BENGIO Y, HINTON G. Deep learning [J] Nature, 2015, 521(7553): 436444.

    [3] LI Y T, TENG F B, XIAN J H et al. Underwater crack pixel-wise identification and quantification for dams via lightweight semantic segmentation and transfer learning[J]. Automation in Construction, 9, 104600(2022).

    [4] DUNG C V, ANH L D. Autonomous concrete crack detection using deep fully convolutional neural network[J]. Automation in Construction, 99, 52-58(2019).

    [6] [6] ZHAO X F, LI S Y. A method of crack detection based on convolutional neural wks[C]Proceedings of the 11th International Wkshop on Structural Health Moniting. Stanfd, CA: DEStech Publications, Inc, 2017.

    [7] LI L F, MA W F, LI L et al. Research on detection algorithm for bridge cracks based on deep learning[J]. Acta Automatica Sinica, 45, 1727-1742(2019).

    [10] [10] LIN T Y, DOLLAR P, GIRSHICK R , et al. Feature pyra wks f object detection[C]Proceedings of the IEEE 2017 Conference on Computer Vision Pattern Recognition. Honolulu, HI, USA: IEEE, 2017.

    [11] [11] LIU S, QI L, QIN H, et al. Path aggregation wk f instance segmentation[C]Proceedings of the 2018 IEEECVF Conference on Computer Vision Pattern Recognition. Salt Lake City, UT, USA : IEEE, 2018: 8759−8768.

    [12] [12] TAN M, PANG R, LE Q V. EfficientDet: scalable efficient object detection[C]Proceedings of the 2020 IEEECVF Conference on Computer Vision Pattern Recognition. Seattle, WA, USA: IEEE, 2020: 1078110790.

    [13] [13] HOWARD, A G, ZHU M L, CHEN B, et al. Mobiles: efficient convolutional neural wks f mobile vision applications [JOL]. arXiv preprint arXiv. (20170417)[20230607]. https:arxiv.gpdf1704.04861.pdf.

    [14] [14] ZHANG X Y, ZHOU X Y, LIN M X, et al. Shuffle: an extremely efficient convolutional neural wk f mobile devices[C]Proceedings of the 2018 IEEECVF Conference on Computer Vision Pattern Recognition. Salt Lake City, UT, USA: IEEE, 2018: 6848−6856.

    [15] [15] HAN K, WANG Y H, TIAN Q, et al. Ghost: me features from cheap operations[C]Proceedings of the 2020 IEEE CVF Conference on Computer Vision Pattern Recognition. Seattle, WA, USA : IEEE, 2020: 1580−1589.

    [16] [16] LI H L, LI J, WEN H B, et al. Slimneck by GSConv: a better design paradigm of detect architectures f autonomous vehicles[JOL]. arXiv. (20220606)[ 20230607]. https:arxiv.gftparxivpapers22062206.02424.pdf.

    [17] [17] CHEN J R, KAO S H, HE H, et al. Run, don''t walk: chasing higher FLOPS f faster neural wks [C]Proceedings of the 2023 IEEECVF Conference on Computer Vision Pattern Recognition. Vancouver, BC, Canada: IEEE, 2023.

    [18] [18] REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: unified, realtime object detection[JOL]. arXiv. (20150608) [20230607]. https:arxiv.gpdf1506.02640.pdf.

    [19] [19] WOO S, PARK J, LEE J Y, et al. CBAM: Convolutional block attention module[C]Proceedings of the 15th European Conference on Computer Vision (ECCV). Munich, Germany: Springer, 2018: 3−19.

    [20] [20] ZHENG Z H, WANG P, LIU W, et al. DistanceIoU loss: Faster better learning f bounding box regression[C]Proceedings of the 34th AAAI Conference on Artificial Intelligence. New Yk Hilton town, New Yk, USA: AAAI Press, 2020, 34(7): 12993−13000.

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    Chong WANG, Yuhui ZHU. Ship crack detection method based on lightweight fast convolution and bidirectional weighted feature fusion network[J]. Chinese Journal of Ship Research, 2024, 19(5): 95

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    Paper Information

    Category: Ship Structure and Fittings

    Received: Jun. 7, 2023

    Accepted: --

    Published Online: Mar. 14, 2025

    The Author Email:

    DOI:10.19693/j.issn.1673-3185.03401

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