Abstract
Recently, many attempts have been made to reduce the time required for payment in various shopping environments. In addition, with advancements in artificial intelligence and IoT technologies, it has become easier to create unmanned environments for shopping, reducing the need for human intervention. This paper proposes a smart shopping cart system based on low-cost IoT equipment and deep learning object detection technology. The system consists of a camera for real-time product detection, an ultrasonic sensor as a trigger, a weight sensor to determine if a product enters or exits the shopping cart, and a smartphone app providing a virtual cart interface. The server uses YOLO, an object detection library, for product recognition. Communication occurs via TCP/IP and HTTP, allowing users to monitor items in their cart and make automatic payments. This system aims to offer a high cost-performance ratio for implementing unmanned stores. Key words: Deep Learning ,Real-time Object Detection, YOLO , Internet of Things , Smart Shopping Cart
Published Version
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