A UAV Object Detection Benchmark for Vision-assisted Powerline Element Inspection

Abstract

Powerline inspection operations involve capturing and inspecting visual footage of powerline elements in electric transmission infrastructures. Current technological advantages in the areas of robotics and machine learning are towards enabling the utilization of completely autonomous Unmanned Aerial Vehicles (UAVs) to carry out such tasks. One of the tasks to be addressed is the robust, precise, and fast powerline object detection problem. To this end, UAVs are required to perform visual object detection autonomously, with high accuracy and fast algorithm execution speed, for providing image regions of interest to be inspected by humans or even be used as input for autonomously controlling the UAV/camera. However, the limited computational resources of the on-board devices of such systems heavily affect the type of neural network architectures that can potentially be deployed. In this work, we study state-of-the-art object detectors in an attempt to find an acceptable trade-off between detection accuracy and inference speed that will allow the exploitation of UAVs for autonomous powerline inspection purposes. To this end, we publicly release a powerline inspection dataset and state a benchmark evaluation with recently proposed object detectors based on deep learning.