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.
翻译:许多电力线公司正在利用无人驾驶飞行器进行检查,而不是通过使其工人爬上高压电线塔而使其处于危险之中。例如,检查的关键任务是探测和分类电力传输线上的资产。然而,与电力线资产有关的公共数据很少,从而阻止了这一领域的更快发展。这项工作提议建立电力线资产数据集,其中包含多个高压电线组件的高分辨率和真实世界图像。它有2 409个附加说明的物体,分为五类:传输塔、喷气塔、航天器、塔板和Stockbridge 水坝,其大小(分辨率)、方向、照明、引线和背景各异。这项工作还利用受欢迎的深物体探测方法进行评估,显示出相当大的改进空间。STNPLAD数据集可在https://github.com/andreluizbvs/PLAD上公开查阅。