Development of Augmented Reality Platform Using Image Processing with Deep Learning Techniques

Abstract

The development and structuring of an augmented reality (AR) platform integrating image processing, deep learning, and spatial tracking techniques have yielded promising results. AR technology, which enhances real-world objects with computer-generated information, holds vast potential for education, entertainment, and industry. Image processing enables real-time object detection, recognition, and tracking within the user's environment, while deep learning models enhance pattern recognition accuracy. Spatial tracking techniques ensure seamless integration of virtual content with the physical world, providing immersive AR experiences. Ant colony optimization (ACO) algorithms further enhance the AR platform's functionality by optimizing object placement and interaction. ACO mimics ant behavior to find optimal paths through graphs, effectively optimizing spatial layout and user interaction patterns in dynamic environments. The integration of ACO into the AR platform facilitates efficient pathfinding for augmented objects and enhances the system's responsiveness to user inputs. The successful implementation of the AR platform underscores its potential to revolutionize various industries, including healthcare, education, and marketing. By leveraging AI-driven algorithms and optimization techniques, AR systems can deliver personalized and engaging experiences that blur the boundaries between the digital and physical worlds. These results pave the way for future advancements in AR technology, driving innovation and reshaping human-computer interaction paradigms.