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Efficient Cross-Modality Insulator Augmentation for Multi-Domain Insulator Defect Detection in UAV Images.

Yue Liu¹, Xinbo Huang^1,2

¹School of Electrical Engineering, Xi'an University of Technology, Xi'an 710054, China.

Sensors (Basel, Switzerland)

|January 23, 2024

Summary

This study introduces an efficient cross-modality insulator augmentation algorithm to improve power system safety. The method enhances insulator defect detection without increasing computational load, benefiting transmission line inspection.

Keywords:

deep learning insulator defect detection transmission line inspection unmanned aerial vehicle (UAV)

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Area of Science:

Electrical Engineering
Computer Vision
Artificial Intelligence

Background:

Regular inspection of insulators is crucial for power system stability.
Unmanned aerial vehicle (UAV) inspection is replacing manual methods for transmission lines.
Deep learning methods for insulator defect detection show promise but face inference complexity challenges.

Purpose of the Study:

To propose an efficient cross-modality insulator augmentation algorithm for multi-domain defect detection.
To address the overfitting problem and inference complexity in existing methods.
To enhance the accuracy of insulator defect detection in real-world scenarios.

Main Methods:

Developed a High-Resolution Insulator Cross-Modality Translation (HICT) module to generate multi-modality images, reducing modality discrepancy.
Introduced a Multi-Domain Insulator Multi-Scale Spatial Augmentation (MMA) module to augment images across different spatial scales.
Integrated fused images and location information to improve defect localization for various scales.

Main Results:

The proposed algorithm achieved superior performance on public UPID and SFID insulator defect datasets.
Demonstrated effective alleviation of the overfitting problem without additional inference resources.
Validated the algorithm's ability to mimic complex scenarios and improve detection accuracy.

Conclusions:

The novel cross-modality augmentation algorithm significantly enhances insulator defect detection precision.
The method offers a valuable approach to improve transmission line inspection efficiency and safety.
This work provides a new perspective for advancing defect detection without increasing computational demands.