Power component detection in UAV via virtual assisted reality mechanisms

Xi Wang; Kun Qian; Xiao Wang

doi:10.1117/12.3055908

9 January 2025 Power component detection in UAV via virtual assisted reality mechanisms

Xi Wang, Kun Qian, Xiao Wang

Author Affiliations +

Proceedings Volume 13486, Fourth International Conference on Computer Vision, Application, and Algorithm (CVAA 2024); 1348626 (2025) https://doi.org/10.1117/12.3055908
Event: Fourth International Conference on Computer Vision, Application, and Algorithm (CVAA 2024), 2024, Chengdu, China

Abstract

Power components, such as electric poles and insulators, play a critical role in ensuring the stability and functionality of UAV (unmanned aerial vehicle) electrical systems. The detection of these components allows for the analysis of potential defects, malfunctions, and weaknesses that may compromise the efficiency and safety of the overall power infrastructure. Annotating real data obtained from power component detection poses inherent difficulties that require meticulous attention to detail. Manual annotation of real data often proves to be time-consuming, error-prone, and subject to various limitations such as human bias and inconsistencies. Moreover, the sheer volume of data to be annotated can further exacerbate these challenges, hindering efficient and accurate analysis. To mitigate the challenges associated with real data annotation, we have devised a mechanism that incorporates virtual data to complement and supplement the real data-driven detection process. By creating a synergy between real and virtual data, the system can simulate various scenarios, augmenting the real data pool. This integration reduces the burden of manual annotation, enhances the accuracy of analysis, and ultimately improves the overall performance of power component detection. The integration of virtual data as an auxiliary tool in power component detection has proven to be a significant breakthrough. By reducing the workload associated with manually annotating real data and enhancing the accuracy of analysis, this approach has the potential to revolutionize the field of power infrastructure detection. Experimental validation shows that when sampling 10% of the virtual data, it can boost the MAP of the real data.

(2025) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Xi Wang, Kun Qian, and Xiao Wang "Power component detection in UAV via virtual assisted reality mechanisms", Proc. SPIE 13486, Fourth International Conference on Computer Vision, Application, and Algorithm (CVAA 2024), 1348626 (9 January 2025); https://doi.org/10.1117/12.3055908

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KEYWORDS

Data modeling

Education and training

Dielectrics

Feature extraction

Object detection

Unmanned aerial vehicles

Virtual reality

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