AE-FPN: adaptive enhance feature learning for detecting wire defects-Reference-Cited by-同舟云学术

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AE-FPN: adaptive enhance feature learning for detecting wire defects

Published:2022-12-19 Issue: Volume: Page:
ISSN:1863-1703
Container-title:Signal, Image and Video Processing
language:en
Short-container-title:SIViP

Author:

Zhang Hui,Du Jianming,Xie Chengjun,Zhang Jie,Qian Shaowei,Li Rui

Funder

Research on new method of inspection and operation sensing and control of power grid equipment and its application

Research and industrialization of key technologies of visual intelligence and edge computing for complex power scenarios

Anhui Provincial Natural Science Foundation under Grant

Publisher

Springer Science and Business Media LLC

Subject

Electrical and Electronic Engineering,Signal Processing

Link

https://link.springer.com/content/pdf/10.1007/s11760-022-02429-3.pdf

Reference45 articles.

1. Jenssen, R., Roverso, D.: Automatic autonomous vision-based power line inspection: a review of current status and the potential role of deep learning. Int. J. Electr. Power Energy Syst. 99, 107–120 (2018)

2. Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017)

3. Kirillov, A., Girshick, R., He, K., Dollár, P.: Panoptic feature pyramid networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 6399–6408 (2019)

4. Jiang, X., Xia, Y., Hu, J., Zhang, Z., Shu, L., Sun, C.: An S-transform and support vector machine (SVM)-based online method for diagnosing broken strands in transmission lines. Energies 4(9), 1278–1300 (2011)

5. Zhao, L., Huang, X., Jia, J., Zhu, Y., Cao, W.: Detection of broken strands of transmission line conductors using fiber Bragg grating sensors. Sensors 18(7), 2397 (2018)

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