Automated Inspection of Monopole Tower Using Drones and Computer Vision

Abstract

Drones are used in a wide range of applications such as manual inspection of mobile towers, transmission lines, and Search and Rescue operations. Traditional methods of using drones for manual inspections can be time and cost consuming. Skilled labor is also required for controlling the drone. Several 'crack detection algorithms' have been developed for detecting cracks but there are still problems with accuracy. In this paper, we propose a computer vision algorithm under the robot operating system (ROS) platform that can detect the Region of Interest (ROI) and analyze images in real time. Drone airtime is reduced as a result of this method. This newly developed system will inspect towers and detect cracks and rusts therein. This method also considers the challenges that occur in manual methods as well as drone capabilities. This system takes the measurement of the detected cracks, and classify the types of rusts found using Deep Learning Techniques.