Adaptive cropping shallow attention network for defect detection of bridge girder steel using unmanned aerial vehicle images-Reference-Cited by-同舟云学术

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Adaptive cropping shallow attention network for defect detection of bridge girder steel using unmanned aerial vehicle images

Published:2023-03 Issue:3 Volume:24 Page:243-256
ISSN:1673-565X
Container-title:Journal of Zhejiang University-SCIENCE A
language:en
Short-container-title:J. Zhejiang Univ. Sci. A

Author:

Mu Zonghan^ORCID,Qin Yong,Yu Chongchong,Wu Yunpeng,Wang Zhipeng,Yang Huaizhi,Huang Yonghui

Publisher

Zhejiang University Press

Subject

General Engineering

Link

https://link.springer.com/content/pdf/10.1631/jzus.A2200175.pdf

Reference45 articles.

1. Ali R, Kang D, Suh G, et al., 2021. Real-time multiple damage mapping using autonomous UAV and deep faster region-based neural networks for GPS-denied structures. Automation in Construction, 130:103831. https://doi.org/10.1016/j.autcon.2021.103831

2. Arivazhagan S, Shebiah RN, Magdalene JS, et al., 2015. Railway track derailment inspection system using segmentation based fractal texture analysis. ICTACT Journal on Image and Video Processing, 6(1): 1060–1065. https://doi.org/10.21917/ijivp.2015.0155

3. Bochkovskiy A, Wang CY, Liao HYM, 2020. YOLOv4: optimal speed and accuracy of object detection. arXiv: 2004.10934. https://doi.org/10.48550/arXiv.2004.10934

4. Box GEP, Cox DR, 1964. An analysis of transformations. Journal of the Royal Statistical Society: Series B (Methodological), 26(2):211–243. https://doi.org/10.1111/j.2517-6161.1964.tb00553.x

5. Cha YJ, Choi W, Büyüköztürk O, 2017. Deep learning-based crack damage detection using convolutional neural networks. Computer-Aided Civil and Infrastructure Engineering, 32(5):361–378. https://doi.org/10.1111/mice.12263

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