<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In order to improve the defect recognition efficiency of transmission lines, the industry is currently using aerial images for automatic visual defect detection to ensure the safe operation of transmission lines. This paper proposes a method for defect recognition from coarse to fine, based on convolutional neural networks and connected domain algorithms, to improve recognition accuracy. The recognition speed is improved by using the knowledge distillation method of target detection networks based on decoupled features, adversarial features, and attention features. It has been found that the optimized recognition model improves the precision rate by 7%, the recall rate by 8%, and the average accuracy rate by 10%. The FPS of the model optimized by knowledge distillation is 62.5, and the average value of the FPS of other versions of this model is 47.35. It is believed that the two optimization ideas introduced in this paper can enhance the previous transmission line defect recognition algorithm in terms of accuracy and recognition speed.</p>
</abstract>

In order to improve the defect recognition efficiency of transmission lines, the industry is currently using aerial images for automatic visual defect detection to ensure the safe operation of transmission lines. This paper proposes a method for defect recognition from coarse to fine, based on convolutional neural networks and connected domain algorithms, to improve recognition accuracy. The recognition speed is improved by using the knowledge distillation method of target detection networks based on decoupled features, adversarial features, and attention features. It has been found that the optimized recognition model improves the precision rate by 7%, the recall rate by 8%, and the average accuracy rate by 10%. The FPS of the model optimized by knowledge distillation is 62.5, and the average value of the FPS of other versions of this model is 47.35. It is believed that the two optimization ideas introduced in this paper can enhance the previous transmission line defect recognition algorithm in terms of accuracy and recognition speed.

Research on defect recognition technology of transmission line based on visual macromodeling

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

In order to improve the defect recognition efficiency of transmission lines, the industry is currently using aerial images for automatic visual defect...

Research on defect recognition technology of transmission line based on visual macromodeling

Online veröffentlicht: 05. Aug. 2024

Seitenbereich: -

Eingereicht: 30. März 2024

Akzeptiert: 28. Juni 2024

DOI: https://doi.org/10.2478/amns-2024-2216

Schlüsselwörter
Transmission line defect recognition, CNN, Connected domain algorithm, Knowledge distillation

© 2024 Yang Li et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Research on defect recognition technology of transmission line based on visual macromodeling

Online veröffentlicht: 05. Aug. 2024

Seitenbereich: -

Eingereicht: 30. März 2024

Akzeptiert: 28. Juni 2024

DOI: https://doi.org/10.2478/amns-2024-2216

SchlüsselwörterTransmission line defect recognition, CNN, Connected domain algorithm, Knowledge distillation

© 2024 Yang Li et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Transmission line defect recognition, CNN, Connected domain algorithm, Knowledge distillation