Drone–Robot to Clean Power Line Insulators

Gonçalves, Rogério Sales; De Oliveira, Murilo; Rocioli, Murilo; Souza, Frederico; Gallo, Carlos; Sudbrack, Daniel; Trautmann, Paulo; Clasen, Bruno; Homma, Rafael

doi:10.3390/s23125529

Cited by 5 publications

(1 citation statement)

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“…With the rapid development of smart grids, as well as the widespread use of computers and intelligent devices, the inspection model using power vision technology for visual processing and analysis of transmission lines has gradually become mainstream [ 6 , 7 ]. Therefore, as a low-cost, short-cycle, and highly maneuverable inspection method, drones are receiving increasing attention in power transmission line inspection [ 8 , 9 , 10 ]. By processing the images of transmission lines obtained through unmanned aerial vehicle (UAV) inspection, it is possible to accurately detect hidden dangers in transmission lines, reduce the incidence of accidents, and improve the reliability of the power grid operating environment [ 11 , 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

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

Liu,

Huang

2024

Sensors

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Regular inspection of the insulator operating status is essential to ensure the safe and stable operation of the power system. Unmanned aerial vehicle (UAV) inspection has played an important role in transmission line inspection, replacing former manual inspection. With the development of deep learning technologies, deep learning-based insulator defect detection methods have drawn more and more attention and gained great improvement. However, former insulator defect detection methods mostly focus on designing complex refined network architecture, which will increase inference complexity in real applications. In this paper, we propose a novel efficient cross-modality insulator augmentation algorithm for multi-domain insulator defect detection to mimic real complex scenarios. It also alleviates the overfitting problem without adding the inference resources. The high-resolution insulator cross-modality translation (HICT) module is designed to generate multi-modality insulator images with rich texture information to eliminate the adverse effects of existing modality discrepancy. We propose the multi-domain insulator multi-scale spatial augmentation (MMA) module to simultaneously augment multi-domain insulator images with different spatial scales and leverage these fused images and location information to help the target model locate defects with various scales more accurately. Experimental results prove that the proposed cross-modality insulator augmentation algorithm can achieve superior performance in public UPID and SFID insulator defect datasets. Moreover, the proposed algorithm also gives a new perspective for improving insulator defect detection precision without adding inference resources, which is of great significance for advancing the detection of transmission lines.

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