STN PLAD: A Dataset for Multi-Size Power Line Assets Detection in High-Resolution UAV Images

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.

Squeezed Deep 6DoF Object Detection Using Knowledge Distillation

The detection of objects considering a 6DoF pose is common requisite to build virtual and augmented reality applications. It is usually a complex task witch requires real-time processing and high precision results for an adequate user experience. Recently, different deep learning techniques have been proposed to detect objects in 6DoF in RGB images but they rely on high complexity networks, requiring a computational power that prevents them to work on mobile devices. In this paper, we propose an approach to reduce the complexity of 6DoF detection networks while maintaining accuracy. We used Knowledge Distillation to teach portables Convolutional Neural Networks (CNN) to learn from a real-time 6DoF detection CNN. The proposed method allows real-time applications using only RGB images while decreasing the hardware requirements. We used the LINEMOD dataset to evaluate the proposed method and the experimental results show that the proposed method reduces the memory requirement almost 99\% in comparison to the original architecture reducing half the accuracy in one of the metrics. Code is available at https://github.com/heitorcfelix/singleshot6Dpose