Augmented visualization using homomorphic filtering and Haar-based natural markers for power systems substations

Abstract This paper presents an approach for annotating real data of power system equipment and has a main goal of improving visualization in outdoor scenarios where lighting presents itself as a problem for the detection of objects. It proposes the creation of object detectors as natural markers using Haar-like features and homomorphic filtering to include real information in an augmented visualization of a substation. The proposed system provides a real-time solution for displaying the data that are acquired from the SCADA/EMS automation system over the real scenario of the substation by providing an augmented visualization. The proposed system achieves an acceptable response time and the object detection step receives updates on each frame from the camera. Thus, it allows the use of augmented reality within operation and maintenance activities in the substation equipment, thereby providing data visualizations at the location where the demand exists instead requiring one to move to the control room to visualize the actual systems status. Equipment detection is performed on the video camera of a mobile device, frame by frame, by using a cascade classifier that is based on Haar-like features for the training and detection processes and by applying homomorphic filtering to reduce illumination problems. The proposed system can be used for training several detectors for substation equipment with the same technique. As a proof of concept, this work presents the results that are obtained using the power transformer. Thus, an augmented reality system prototype was developed that achieved good detection rates, thereby showing that the use of theses features is promising for augmented reality implementation in the daily routines of an electrical company.