STN PLAD: A Dataset for Multi-Size Power Line Assets Detection in High-Resolution UAV Images
This provides a new dataset for power line companies to improve automated inspection processes, but it is incremental as it primarily addresses a data gap rather than a methodological breakthrough.
The authors tackled the scarcity of public datasets for power line asset detection by introducing the STN PLAD dataset, which contains 2,409 annotated objects across five classes in high-resolution UAV images, and they evaluated it with deep object detection methods, showing significant room for improvement.
Many power line companies are using UAVs to perform their inspection processes instead of putting their workers at risk by making them climb high voltage power line towers, for instance. A crucial task for the inspection is to detect and classify assets in the power transmission lines. However, public data related to power line assets are scarce, preventing a faster evolution of this area. This work proposes the Power Line Assets Dataset, containing high-resolution and real-world images of multiple high-voltage power line components. It has 2,409 annotated objects divided into five classes: transmission tower, insulator, spacer, tower plate, and Stockbridge damper, which vary in size (resolution), orientation, illumination, angulation, and background. This work also presents an evaluation with popular deep object detection methods, showing considerable room for improvement. The STN PLAD dataset is publicly available at https://github.com/andreluizbvs/PLAD.