Smart shopping cart using OpenCV-Python

• Published in: AIP conference proceedings Vol. 3131; no. 1
• Main Authors: Kanumuri, Chalapathi Raju, Marapatla, Ajay Dilip Kumar, Varma, Kothapalli Phani, Torthi, Ravichandra, Sri Harsha, C. H.
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Melville American Institute of Physics 19.09.2024
• Subjects: Algorithms; Artificial intelligence; Cameras; Deep learning; Industry 4.0; Machine learning; Object recognition; Payment systems; Real time; Shopping
• ISSN: 0094-243X; 1551-7616
• DOI: 10.1063/5.0229730

In recent times, there have been numerous efforts to streamline the billing and payment processes in various shopping environments. Additionally, with the advancements in artificial intelligence technology and the increasing affordability of IoT devices during the era of the 4th Industrial Revolution, it has become more feasible to create unmanned environments that can save users’ time. Therefore, we propose a smart shopping cart system that leverages deep learning object detection technology using OpenCV and Python. The proposed smart cart system is composed of a camera that enables real-time product detection. By employing deep learning algorithms, such as Yolov3, the camera captures and identifies the products, adding them to the cart automatically. Furthermore, the system generates a total bill based on the detected items. Through the camera’s detection capabilities, users can conveniently view the list of items present in the smart cart, and the payment process is automated. The advantages of the proposed smart cart system include its ability to create unmanned stores that are highly efficient, accurate, and cost-effective. By integrating deep learning object detection technology and IoT devices, we can achieve a seamless shopping experience that minimizes the time required for billing and payment.