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A maker-built smart grocery cart can use a camera and an Edge Impulse object-detection model to track a small set of products, then send the selected cart list to a locally hosted web app. Kutluhan Aktar’s prototype uses an OpenMV Cam H7 for image capture and inference, a Beetle ESP32-C3 for Wi-Fi communication, and a joystick-driven interface for confirming whether a detected item is being added or removed. It demonstrates a possible cart retrofit—not a validated, ready-for-store checkout system.
What the smart cart does
The project aims to recognize products as a shopper adds or removes them from a cart. The camera runs the detector locally, while the ESP32-C3 connects the cart to a web application. A small display shows the camera view, detections, and menu; the shopper uses a joystick to select an add or remove action. An RFID reader scans a key tag assigned to the cart.
When the shopper finishes, the web application updates the cart’s item list and emails that list with a payment link. The creator documents a LattePanda 3 Delta 864 hosting the application and MariaDB database. The workflow therefore combines local visual recognition with connected list management and email; it is not a fully self-contained checkout transaction.
Project documentation and build resources describe the system and its components. The creator also published the build on Hackaday.io.
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How item recognition and cart updates work
- Capture: The OpenMV Cam H7 captures an image of a product near the cart.
- Detect: Its Edge Impulse object-detection model identifies a label from the supported set.
- Choose an action: The shopper uses the joystick and display interface to indicate whether the detected item should be added or removed.
- Send the update: The OpenMV communicates with the Beetle ESP32-C3 over serial; the ESP32-C3 makes an HTTP request to the web application.
- Finish the session: The application maintains the item list and, when the shopper finishes, emails the list and a payment link.
The RFID reader provides a way to identify the cart through its assigned tag. The documented interface also includes an RGB LED and buzzer, though the project descriptions do not establish detailed feedback behavior for each.
What the Edge Impulse model recognizes—and how well
The public Edge Impulse project lists five product labels: Barilla, milk, Nutella, Pringles, and Snickers. Its dashboard reports 70 collected data items. For the unoptimized float32 model, the dashboard reports 100.0% validation-set accuracy and 90.0% test-set accuracy. These are project-reported results on the project’s data, not independently reproduced results or a forecast of accuracy in a store. The public model dashboard lists the project and its metrics.
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The project’s feature article describes a roughly 70-image dataset and a 90% held-out average accuracy result, while also saying more samples would be needed for retail use. The current public dashboard is the more direct source for the listed count and test metric. Edge Impulse’s January 12, 2023 feature explains the prototype’s goals and data limitations.
Do not interpret these figures as proof that the cart recognizes arbitrary groceries or works reliably across different packaging, lighting, viewing angles, and occlusion. The published material does not establish those capabilities or in-store performance.
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On-device performance estimate is a separate measure
The dashboard also lists an estimate for a quantized int8 model on a Cortex-M4F 80 MHz target: 2,376 ms latency, 631.0K peak RAM usage, and 74.8K flash usage. Those figures describe that model configuration and target estimate; they are not the float32 model’s accuracy results, nor a measured end-to-end response time for the complete cart.
Hardware and software in the prototype
| Part | Role in the build |
|---|---|
| OpenMV Cam H7 | Captures images and runs the object detector locally. Edge Impulse describes it as an Arm Cortex-M7 board at 480 MHz with 1 MB SRAM and a built-in image sensor; it has no Wi-Fi radio. |
| DFRobot Beetle ESP32-C3 | Receives serial messages from the OpenMV and provides wireless communications for HTTP requests to the web application. |
| ST7735 1.8-inch color TFT and analog joystick | Show the camera view, recognition results, and menu; allow the shopper to choose an add or remove action. |
| MFRC522 reader and RFID key tag | Read the tag associated with a cart. |
| LattePanda 3 Delta 864 and MariaDB | Host the documented web application and database. |
| Custom PCB, microSD card, RGB LED, buzzer, power jack, external battery, jumper wires | Supporting electronics and power components listed by the creator. |
| 3D-printed enclosure components | The creator documents using Creality printers and a Creality Sonic Pad to produce enclosure parts. |
The project documentation also lists an optional DFRobot touch display. The creator provides project code, PCB Gerbers, 3D-part STL files, and an OpenMV firmware export through the project page and repository. These are documented design choices; verify board revisions, connections, and software compatibility before trying to reproduce the build.
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The public Edge Impulse project is listed as version 1 with an Apache 2.0 license and can be cloned by an Edge Impulse account holder. That license applies to the listed project, not automatically to every hardware component or external service used in a recreation.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Can you convert a regular shopping cart?
The project is framed as a retrofit: it mounts sensing and interface hardware to a conventional cart rather than requiring each product to carry an RFID tag. Its camera-based approach avoids tagging each recognized product, but it still requires cart-mounted electronics, power, communications, a backend application, and a way to handle items the model cannot recognize.
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The available project material does not establish a complete installation procedure for every cart, a total build cost, or a measured comparison against RFID or weight-sensor alternatives. When assessing another smart-cart design, compare sensing method, tagging requirements, processing location, connectivity, retrofit burden, product coverage, independent accuracy, response time, power and memory needs, maintenance, and payment integration.
What the prototype does not prove
- Broad product coverage: The published detector has five labels and 70 collected data items, not a general grocery catalogue.
- Retail reliability: The reported test accuracy comes from the project’s held-out data. Independent replication and store-condition performance are not established.
- Automatic checkout: The documented flow relies on shopper selection, a web application, and an emailed payment link. The sources do not show a deployed system that eliminates checkout lines.
- End-to-end privacy: Local inference avoids sending model inference to the cloud, according to Edge Impulse’s feature. The overall prototype still connects to a web app and email workflow, so that point alone does not establish privacy for the entire shopping session.
In the creator’s December 27, 2022 Hackaday.io description, Kutluhan Aktar wrote: “After completing my data set including various food retail products, I built my object detection model with Edge Impulse to detect products added or removed to/from the grocery cart.” The statement captures the intended function; the project’s limited classes and dataset define the demonstrated scope.
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