Abstract
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.