Lightweight network for insulator fault detection based on improved YOLOv5

IF 3.2 4区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Connection Science Pub Date : 2024-02-09 DOI:10.1080/09540091.2023.2284090

Dehua Weng, Zhiliang Zhu, Zhengbing Yan, Moran Wu, Ziang Jiang, Nan Ye

引用次数: 0

Abstract

Severe damage to insulators can hinder the daily operation of the power system. Current fault diagnosis methods heavily depend on manual visual inspection, leading to inefficiency and inaccuracies....

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基于改进型 YOLOv5 的轻量级绝缘子故障检测网络

绝缘子的严重损坏会妨碍电力系统的日常运行。目前的故障诊断方法严重依赖人工目测，导致效率低下且不准确....。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Connection Science 工程技术-计算机：理论方法

CiteScore

6.50

自引率

39.60%

发文量

审稿时长

3 months

期刊介绍： Connection Science is an interdisciplinary journal dedicated to exploring the convergence of the analytic and synthetic sciences, including neuroscience, computational modelling, artificial intelligence, machine learning, deep learning, Database, Big Data, quantum computing, Blockchain, Zero-Knowledge, Internet of Things, Cybersecurity, and parallel and distributed computing. A strong focus is on the articles arising from connectionist, probabilistic, dynamical, or evolutionary approaches in aspects of Computer Science, applied applications, and systems-level computational subjects that seek to understand models in science and engineering.