Abstract

Existing publicly available datasets for insulator recognition primarily focus on overhead transmission lines and are commonly acquired using unmanned aerial vehicles. As a result, they often do not reflect the visual complexity of high-voltage substation environments, which are characterized by dense equipment arrangements, structured industrial backgrounds, frequent occlusions, and substantial variation in object scale. To address this gap, we present UVInsDet, a real-world dataset for insulator-string detection and instance segmentation collected during ground-based robotic inspections of an operational 220 kV substation. The dataset comprises 591 visible-spectrum RGB images acquired using a narrow-angle diagnostic inspection camera and contains 1415 manually annotated insulator-string instances represented by pixel-wise segmentation masks. The images cover daytime and nighttime conditions, varying weather scenarios, different viewing angles, and both target-object and negative samples corresponding to realistic inspection workflows. The dataset includes annotations for glass and porcelain insulator strings and provides data in both LabelMe and COCO formats. UVInsDet is intended as a specialized resource for computer vision research in industrial inspection environments. The dataset can support the development and evaluation of object detection and instance segmentation methods, studies of small-object recognition in complex scenes, robustness assessment under varying observation conditions, domain adaptation research, and the development of intelligent monitoring and inspection systems for power infrastructure.

IPC Classification

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