References

moitvivt

Моделирование, оптимизация и информационные технологии

Modeling, Optimization and Information Technology

2310-6018

Издательство

10.26102/2310-6018/2021.35.4.035

1115

Детектирование дефектов неисправных элементов линий электропередач при помощи нейронных сетей семейства YOLO

Detection of defects in faulty elements of power lines using neural networks of YOLO

0000-0002-9121-894X

Астапова

Марина Алексеевна

Astapova

Marina Alekseevna

marinaastapova55@gmail.com aff-1

0000-0002-7032-0291

Уздяев

Михаил Юрьевич

Uzdiaev

Mikhail Yurievich

m.y.uzdiaev@gmail.com aff-2

Санкт-Петербургский Федеральный исследовательский центр Российской академии наук St. Petersburg Federal Research Center of the Russian Academy of Sciences

01 01 2026

1 1

10.26102/2310-6018/2021.35.4.035

2026

This work is licensed under a Creative Commons Attribution 4.0 International License

В настоящее время визуальная диагностика состояния элементов линий электропередач (ЛЭП) является сложной и трудоемкой процедурой. В целях повышения эффективности и снижения трудозатрат этой процедуры наиболее перспективным является применение беспилотных летательных аппаратов, оборудованных системами компьютерного зрения, выполняющих автоматическую детекцию поврежденных элементов ЛЭП. Для повышения качества детекции поврежденных участков ЛЭП системами компьютерного зрения наиболее перспективно применение современных глубоких нейросетевых архитектур. Однако, вопрос применения таких архитектур в обозначенной задаче недостаточно освещен в современных исследованиях. Особо остро стоит вопрос сравнения различных нейронных сетей и выявления значимых различий в их результатах. Данная статья посвящена сравнительному анализу современных нейросетевых детекторов YOLOv3 и YOLOv4, а также их сокращенных версий (YOLOv3-tiny и YOLOv4-tiny) в задаче детекции дефектов ЛЭП. Приводятся результаты обучения этих детекторов на наборе данных CPLID, а также статистическое сравнение результатов YOLOv3 и YOLOv4 посредством процедуры кросс-валидации. Детекторами были показаны высокие результаты точности детекции (mAP@0,50=0,97±0,03; mAP@0,75=0,78±0,04), а также статистически значимые различия в этих результатах. Сравнительный анализ результатов показал, что применение более простой нейронной сети YOLOv3 является более перспективным в задаче детекции дефектов ЛЭП.

Currently, visual state diagnostics of power transmission lines (PTL) elements is a complex and time-consuming procedure. In order to increase the efficiency and reduce the labor costs of this undertaking, the most promising measure is the use of unmanned aerial vehicles equipped with computer vision systems that automatically detect damaged elements of power lines. For the purposes of improving the detection quality of power lines damaged areas by computer vision systems, the application of modern deep neural network architectures would be most effective. However, the problem of utilizing such architectures in the aforementioned task is not sufficiently covered in modern research. The issue of comparing various neural networks and identifying substantial differences in their results is especially acute. This article is devoted to a comparative analysis of modern neural network detectors YOLOv3 and YOLOv4 as well as their reduced versions (YOLOv3-tiny and YOLOv4-tiny) in terms of detecting power transmission line defects. The results of training these detectors on the CPLID dataset are presented along with statistical comparison of the YOLOv3 and YOLOv4 results by means of the cross-validation procedure. The detectors displayed high rates of detection accuracy (mAP @ 0.50 = 0.97 ± 0.03; mAP @ 0.75 = 0.78 ± 0.04) and statistically significant distinctions in these results. A comparative analysis of the findings has revealed that the employment of a simpler neural network YOLOv3 has more potential when applied to detection of power transmission line defects.

беспилотный летательный аппарат обследование высоковольтных линий электропередач обнаружение неисправностей определение дефектов нейронные сети YOLOv3 YOLOv4

unmanned aerial vehicle inspection of high-voltage power lines fault detection defect detection neural networks YOLOv3 YOLOv4

Работа выполнена при финансовой поддержке РФФИ в рамках научного проекта №20-08-01056 А

This work was carried out with the financial support of the Russian Foundation for Basic Research within the framework of scientific project No. 20-08-01056 A.

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The authors declare that there are no conflicts of interest present.