Bianchi Reinaldo A C, Ferraz Hericles F, Gonçalves Rogério S, Moura Breno, Sudbrack Daniel E T, Merini Antoniele, Machado Maria de Lourdes G, Pires Rodrigo, Homma Rafael Z
Electrical Engineering Department, Centro Universitario FEI, São Bernardo do Campo 09850-901, São Paulo, Brazil.
School of Mechanical Engineering, Universidade Federal de Uberlândia, Uberlândia 38400-902, Minas Gerais, Brazil.
Data Brief. 2024 Jun 27;55:110688. doi: 10.1016/j.dib.2024.110688. eCollection 2024 Aug.
High-voltage power line insulators are crucial for safe and efficient electricity transmission. However, real-world image limitations, particularly regarding dirty insulator strings, delay the development of robust algorithms for insulator inspection. This dataset addresses this challenge by creating a novel synthetic high-voltage power line insulator image database. The database was created using computer-aided design softwares and a game development engine. Publicly available CAD models of high-voltage towers with the most common insulator types (polymer, glass, and porcelain) were imported into the game engine. This virtual environment allowed for the generation of a diverse dataset by manipulating virtual cameras, simulating various lighting conditions, and incorporating different backgrounds such as mountains, forests, plantation, rivers, city and deserts. The database comprises two main sets: The Image Segmentation Set, which includes 47,286 images categorized by insulator material (ceramic, polymeric, and glass) and landscape type (mountains, forests, plantation, rivers, city and deserts). Moreover, the Image Classification Set that contains 14,424 images simulating common insulator string contaminants: salt, soot, bird excrement, and clean insulators. Each contaminant category has 3,606 images divided into 1,202 images per insulator type. This synthetic database offers a valuable resource for training and evaluating machine learning algorithms for high-voltage power line insulator inspection, ultimately contributing to enhanced power grid maintenance and reliability.
高压电力线绝缘子对于安全高效的电力传输至关重要。然而，现实世界中的图像存在局限性，尤其是在脏污绝缘子串方面，这延缓了用于绝缘子检测的强大算法的开发。该数据集通过创建一个新颖的合成高压电力线绝缘子图像数据库来应对这一挑战。该数据库是使用计算机辅助设计软件和游戏开发引擎创建的。具有最常见绝缘子类型（聚合物、玻璃和陶瓷）的高压塔的公开可用CAD模型被导入到游戏引擎中。这个虚拟环境通过操纵虚拟相机、模拟各种光照条件以及纳入不同背景（如山脉、森林、种植园、河流、城市和沙漠）来生成多样化的数据集。该数据库包括两个主要集合：图像分割集，其中包含47286张按绝缘子材料（陶瓷、聚合物和玻璃）和景观类型（山脉、森林、种植园、河流、城市和沙漠）分类的图像。此外，图像分类集包含14424张模拟常见绝缘子串污染物的图像：盐、烟灰、鸟粪和清洁绝缘子。每个污染物类别有3606张图像，每种绝缘子类型分为1202张图像。这个合成数据库为训练和评估用于高压电力线绝缘子检测的机器学习算法提供了宝贵资源，最终有助于提高电网维护和可靠性。
