On the Detection of Powerline Elements With Efficient Transformers

Powerline inspection operations involve capturing and inspecting visual (RBG/thermal/LiDAR) footage of powerline elements from elevated positions above and around the powerline, which are currently performed with the help of helicopters and/or Unmanned Aerial Vehicles (UAV). 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. End-to-end Object Detection with Transformers (DETR) was a recently introduced method that demonstrates time and accuracy advances with respect to other detectors. However, this architecture comes with some computational complexity issues, which can mainly be attributed to Transformer encoder/decoder components, limiting its applicability in fast processing high-resolution feature maps and long sequences in general. To address these issues, we propose incorporating low-complexity Transformer implementations and evaluate them in a recently captured powerline detection dataset.