Advancements in augmented reality technology: Creating immersive brand experiences through interactive graphic design elements

Abstract

In today’s competitive digital landscape, brands constantly require innovative ways to engage consumers and generate memorable experiences. Augmented reality (AR) has emerged as a powerful tool for transforming traditional brand interactions into immersive, interactive experiences. However, conventional methods often fail to attain high realism, faultless interaction, and adaptive consumer engagement. To address these challenges, this research addresses an AR-based technology that develops the creation of immersive brand experiences through interactive graphic design elements. The Dynamic Dung Beetle Optimizer-Driven Stacked Convolutional Neural Network (DDB-Stacked CNN) is employed for image recognition and AR product design optimization, enhancing user engagement in branding. To ensure high-quality AR-driven graphic design elements, image datasets, consumer interaction data, and branding elements were gathered from various sources. The collected data was preprocessed using image enhancement techniques to improve clarity, while a Kalman filter was applied to reduce noise. Dynamic Dung Beetle Optimization (DDBO) is a global search capability that enables real-time optimization of AR-driven interactive design elements, allowing brands to deliver personalized and visually compelling experiences. The framework is implemented in Python, and the findings indicate that the proposed model significantly increases user engagement and improves brand recall and accuracy compared to conventional techniques. This research highlights the transformative potential of deep learning (DL)-enhanced AR graphic design in fostering stronger brand connections, setting a new benchmark for immersive and interactive brand experiences in the digital age.