A Deep Learning-Based Object Representation Algorithm for Smart Retail Management

This study underscores the vital role of object representation and detection in smart retail management systems for optimizing customer experiences and operational efficiency. The literature review reveals a preference for deep learning techniques, citing their superior accuracy compared to traditional methods. While acknowledging the challenges of achieving high accuracy and low computation costs simultaneously in deep learning-based object representation, the paper proposes a solution using the YOLOv7 framework. In order to navigate the ever-changing landscape of smart retail technologies, the study clarifies the potential scalability and flexibility of deep learning approaches. The method employs a custom dataset, and experimental results demonstrate the model’s efficacy, showcasing accurate results and enhanced performance in various experiments and analyses.