图像数据集的概述 深度学习应用在电力基础设施诊断中的应用
Bogdan Ruszczak1, Paweł Michalski1, Michał Tomaszewski1
1Department of Computer Science, Opole University of Technology, 45-758 Opole, Poland.
Sensors (Basel, Switzerland)
|August 26, 2023
概括
自动化电力线路检查需要深度学习的专用数据集. 这项研究回顾了现有的数据集,并介绍了一系列新的标记图像,用于电力线绝缘体检查.
科学领域:
- 工程 工程师 工程师 工程师
- 计算机科学 计算机科学
背景情况:
- 电力行业依赖于广泛的基础设施,需要定期维护.
- 手动检查电力基础设施是劳动密集型和具有挑战性的,因为广泛的地理分布.
- 深度学习的进步为自动化电力线路检查过程提供了潜力.
研究的目的:
- 审查现有的机器学习数据集,与电力基础设施检查相关.
- 突出领域特定数据集的必要性,以实现有效的深度学习应用.
- 介绍和讨论电力线绝缘体的新型数据集的潜在应用.
主要方法:
- 关于机器学习和深度学习数据集的文献综述.
- 对电力基础设施的选定图像数据库进行比较分析.
- 原始数据集的描述和介绍,包括2630个电力线绝缘体的高分辨率标记图像.
主要成果:
- 识别和比较了电力基础设施领域特定数据集的各种存储库.
- 强调对专业领域深度学习中精心策划,专家标记的数据集的关键需求.
- 介绍了一套新的高分辨率标记图像数据集,专注于电力线绝缘器.
结论:
- 专业化,高质量的数据集对于推进电力部门维护的深度学习至关重要.
- 新推出的绝缘体图像数据集为开发自动化检查工具提供了重大潜力.
- 进一步的研究可以利用这一数据集来提高电力基础设施监测的效率和准确性.
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