Enhancing Autonomous Retail Checkout with Computer Vision and Deep Reinforcement Learning Algorithms

Abstract

This research paper explores the integration of computer vision and deep reinforcement learning algorithms to improve the efficiency and accuracy of autonomous retail checkout systems. The proposed framework leverages advanced image recognition techniques and reinforcement learning strategies to address common challenges faced in retail environments, such as product misidentification and system adaptability to varying merchandise layouts. The study employs a convolutional neural network (CNN) for precise product recognition and classification, which is combined with a deep Q-network (DQN) to optimize decision-making processes in dynamic checkout scenarios. A comprehensive dataset comprising images of diverse retail products under different lighting conditions and orientations is utilized to train and validate the model. Experiments demonstrate that the integrated system achieves a 95% accuracy rate in product identification and reduces the average checkout time by 30% compared to traditional barcode-based systems. Additionally, the system exhibits robust performance in adapting to novel products without necessitating extensive retraining. The findings suggest that the fusion of computer vision and reinforcement learning can significantly enhance the functionality and user experience of autonomous checkout systems, offering a scalable solution for modern retail operations.