Design and development of a Virtual Window with industrial and civil applications

Abstract

The recent advances in deep learning algorithms and computer vision applications have prompted research into their application in various problem domains, including industrial and civil applications. This area o ers a unique opportunity to develop applications that were not previously Possible without computer vision technology. One such application is the replacement of conventional windows with digital smart windows that provide end-users with access to natural scenery. This technology has potential uses in military contexts and meetings, among others. This research focuses on a novel approach to implementing this technology in industrial settings using a real-time face detection algorithm to display natural scenery on a digital screen in a way that provides a user experience similar to looking out a real window. The study involved testing multiple methods and algorithms to identify the fastest and most ef- cient approach that can be implemented purely through software methods, thereby reducing the need for costly sensors and hardware like what you may nd in VRs with expensive sensors. Given the paramount signi cance of both execution speed and detection accuracy in our project, we have made a deliberate decision to utilize the most e cient real-time face detection algorithm available. This algorithm, known as Yolov7, excels in achieving swift processing while maintaining a high level of preci- sion.Details of how this model excels compared to other real-time models can be found in Chapter 3. YOLO represents a singular stage detector that adeptly handles object identi cation and classi cation within a single iteration of the network. Although various single stage detection models exist, YOLO consistently demonstrates supe- rior performance in terms of both speed and accuracy. By approaching the detection task as a single-shot regression method for identifying bounding boxes, YOLO mod- els exhibit remarkable swiftness and compactness, rendering them highly amenable to e cient training and deployment, particularly on resource-constrained edge devices. The algorithm employed in this study was utilized for the dual objectives of detect- ing facial features and identifying landmarks. To cater to the speci c requirements of the project, a custom dataset was employed during the training phase of the algo- rithm. Rather than undertaking the task of creating a novel algorithm, our approach involved identifying the latest and most e cient algorithm, which we subsequently employed in our application. Consequently, we were able to divert more resources towards enhancing the software capabilities of our application, such as accurately estimating the user's head orientation and focusing on related aspects.