Authors: Sidhant Chadha
Abstract: Vision-based object recognition has emerged as a transformative technology in modern retail, revolutionizing how products are identified, tracked, and managed across the supply chain. Leveraging computer vision and deep learning techniques, these systems enable automated product detection, shelf monitoring, customer behavior analysis, and inventory management with high precision and speed. This study explores the design and implementation of vision-based object recognition systems within retail environments, emphasizing the role of convolutional neural networks (CNNs), transfer learning, and real-time image processing frameworks. By integrating cameras, sensors, and AI-driven analytics, retailers can enhance operational efficiency, minimize human error, and provide personalized shopping experiences. The paper also examines challenges such as occlusion, lighting variation, and scalability, along with potential solutions through model optimization and data augmentation. The findings suggest that vision-based recognition systems are key enablers of intelligent retail automation, contributing significantly to the advancement of smart retail ecosystems and Industry 4.0 integration.
DOI: https://doi.org/10.5281/zenodo.17439901
