Proceedings of the 2022 International Conference on Computer Science, Information Engineering and Digital Economy (CSIEDE 2022)

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Detection Method of Conductor Strand Defects Based on Multi-modal Data Fusion

Authors
Jiehui Wu1, Jianrong Zhang1, Liang Fan2, Lei Zhang2, *, Huafeng Su1, Jinduo Zhou1, Guanke Liu1, Zhongyu Li1, Jianzhong Li1, Zhibin He1
1Dongguan power supply bureau, Guangdong Power Grid Corporation, Dongguan, China
2department of Data, Guangzhou zhongke Intelligent Inspection technology Co., Ltd, Guangzhou, China
*Corresponding author. Email: zhanglei@igiai.com
Corresponding Author
Lei Zhang
Available Online 30 December 2022.
DOI
10.2991/978-94-6463-108-1_84How to use a DOI?
Keywords
UAV object detection; UNet image segmentation; multi-modal data fusion; conductor strand defects detection
Abstract

Combined with the advantages of infrared image and visible image, this paper proposes a multi-modal data fusion method for detecting the wire strand defects. The segmentation performance of UNet model in images with poor illumination conditions is improved; it solves the problem that the contrast between the wire and the background in the infrared image is low and it is difficult to distinguish, and reduces the false alarm rate of the loose detection. The experimental results show that this method can be used in UAV patrol images under different lighting conditions, which is more conducive to embedded in UAV for all-weather intelligent patrol, and has the advantages of high recall rate and low false detection rate, compared with the reference method using only infrared or visible light images.

Copyright
© 2022 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2022 International Conference on Computer Science, Information Engineering and Digital Economy (CSIEDE 2022)
Series
Advances in Computer Science Research
Publication Date
30 December 2022
ISBN
978-94-6463-108-1
ISSN
2352-538X
DOI
10.2991/978-94-6463-108-1_84How to use a DOI?
Copyright
© 2022 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Jiehui Wu
AU  - Jianrong Zhang
AU  - Liang Fan
AU  - Lei Zhang
AU  - Huafeng Su
AU  - Jinduo Zhou
AU  - Guanke Liu
AU  - Zhongyu Li
AU  - Jianzhong Li
AU  - Zhibin He
PY  - 2022
DA  - 2022/12/30
TI  - Detection Method of Conductor Strand Defects Based on Multi-modal Data Fusion
BT  - Proceedings of the 2022 International Conference on Computer Science, Information Engineering and Digital Economy (CSIEDE 2022)
PB  - Atlantis Press
SP  - 755
EP  - 766
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-108-1_84
DO  - 10.2991/978-94-6463-108-1_84
ID  - Wu2022
ER  -