Hardware: NVIDIA Jetson Orin Nano
Application: Streamline Shopping Journey
Industry: Smart Retail
Deployment Location: U.S
In the fast-paced world of retail, where convenience takes charge, we always see that the checkout process has long been a bottleneck in the shopping experience. The traditional method of queuing up at a cashier or self-checkout kiosk, fumbling with items, and scanning barcodes has left customers yearning for a more efficient and seamless solution. As our lives grow busier and time becomes an ever more precious commodity, the need for a revolutionary change in the way we shop has become apparent.
In the previous shopping landscape, we may encountered a series of formidable challenges that demanded innovative solutions. Firstly, the scourge of checkout line congestion often left them frustrated, enduring long queues, especially during peak shopping hours. The traditional checkout process, reliant on outdated methods, was a time-consuming ordeal that sorely needed a solution. Furthermore, customers usually were confused about navigating the exact location of products within the store. On the backend, supermarket managers faced the complex challenge of maintaining seamless inventory management. Traditional manual tracking was a laborious and error-prone task, which often resulted in stockouts or overstocked shelves.
To streamline the checkout process and eliminate customer waiting times, this intelligent checkout system embedded in the shopping cart employs state-of-the-art object detection and image recognition algorithms. It comprises a visual settlement platform equipped with a camera, a core NVIDIA Jetson Orin Nano, weight sensors, and a display. The camera captures real-time external images and feeds them into the core processor, which utilizes sophisticated core processing algorithms for rapid and precise item identification. The results are displayed on the customer-facing display, allowing shoppers to view a detailed consumption list and proceed to the next checkout step seamlessly.
In parallel, the system incorporates a robust machine-learning framework. It begins by collecting a comprehensive image dataset, utilizing object detection models to identify individual products. Subsequently, a classification model extracts distinctive features, creating a feature vector set. Vector retrieval techniques are then applied to achieve highly accurate image recognition. This process ensures that items, including barcoded and non-barcoded products, are detected and processed with exceptional precision.
Moreover, it also enhances the in-store shopping experience by utilizing the display panel to help customers effortlessly locate products. With the store’s comprehensive commodity database at their fingertips, customers can pinpoint the exact location of each item, reducing confusion and saving time. Simultaneously, the system provides invaluable real-time insights for store inventory management. This enables store managers to make data-driven decisions, promptly restocking or substituting products as needed, thereby eliminating excess inventory buildup and product shortages.
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