A new design of a flying robot, with advanced computer vision techniques to perform self-maintenance of smart grids
This addresses maintenance automation for smart grid operators, but it appears incremental as it builds on existing computer vision and robotics techniques.
The paper tackles the problem of automating smart grid maintenance by designing a flying robot equipped with thermal and aerial vision sensors, and it achieves component recognition using a new region proposal method based on Discrete Wavelet Transform combined with a Convolutional Neural Network for classification.
In this paper, we present a full design of a flying robot to investigate the state of power grid components and to perform the appropriate maintenance procedures according to each fail or defect that could be recognized. To realize this purpose; different types of sensors including thermal and aerial vision-based systems are employed in this design. The main features and technical specifications of this robot are presented and discussed here in detail. Some essential and advanced computer vision techniques are exploited in this work to take some readings and measurements from the robot's surroundings. From each given image, many sub-images containing different electrical components are extracted using a new region proposal approach that relies on Discrete Wavelet Transform, to be classified later by utilizing a Convolutional Neural Network.