Power line insulator defect detection using CNN with dense connectivity and efficient attention mechanism-Reference-Cited by-同舟云学术

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Power line insulator defect detection using CNN with dense connectivity and efficient attention mechanism

Published:2023-09-07 Issue: Volume: Page:
ISSN:1380-7501
Container-title:Multimedia Tools and Applications
language:en
Short-container-title:Multimed Tools Appl

Author:

Tian XiuXia,Zhang Mengting^ORCID,Lu Guanyu

Funder

National Natural Science Foundation of China

State Grid Gansu Electric Power Company

Shanghai Engineering Research Center on Big Data Management

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Hardware and Architecture,Media Technology,Software

Link

https://link.springer.com/content/pdf/10.1007/s11042-023-15522-7.pdf

Reference31 articles.

1. Amin M (2003) North America’s electricity infrastructure: are we ready for more perfect storms? IEEE Secur 1(5):19–25

2. Cao Y, Xu J, Lin S, Wei F, Hu H (2019) Gcnet: non-local networks meet squeeze-excitation networks and beyond. In: Proceedings of the IEEE/CVF international conference on computer vision workshops

3. Chen L-C, Papandreou G, Kokkinos I, Murphy K, Yuille A L (2017) Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Trans Pattern Anal Mach Intell 40(4):834–848

4. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778

5. He H, Hu Z, Wang B, Luo D, Lee W-J, Li J (2020) A contactless zero-value insulators detection method based on infrared images matching. IEEE Access 8:133882–133889

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