Whitening Transformation inspired Self-Attention for Powerline Element Detection

Abstract

Powerline inspection operations involve capturing and inspecting visual footage of powerline elements from elevated positions above and around the powerline and are currently performed with the help of helicopters and/or Unmanned Aerial Vehicles (UAVs). Current technological advances in the areas of robotics and machine learning are towards enabling fully autonomous operations. To this end, one of the tasks to be addressed is the robust, precise and fast powerline object detection problem. Recently introduced Transformer-based object detection methods demonstrate time and accuracy advances with respect to previous works. In this work, we present an enhanced Transformer-based architecture that further improves the state-of-the-art by incorporating a content-specific object query generator and by substituting the original attention operation with a whitening-inspired transformation at certain stages of the architecture. We evaluate our method in a recently captured powerline detection dataset and we show that our novel contributions offer a significant boost regarding detection accuracy.