AutoBill - An AI Powered Instant Checkout System | Edge Impulse | Raspberry Pi | Coders Cafe

Coders Cafe

10 Jan 202208:30

Summary

TLDRAuto Bear is an automated checkout system designed for small retail stores, utilizing computer vision and deep learning for contact-free, instant item recognition. The system is constructed with plywood, load cells for weight measurement, an amplifier module, a camera for AI object detection, and LED lighting for visibility. The software includes load cell calibration and machine learning model training with high accuracy, all controlled by a Raspberry Pi. The project's detailed instructions and code are available for replication.

Takeaways

🚀 Auto Bear is an AI-powered instant checkout system designed for smaller retail stores.
🔍 It uses computer vision and deep learning to visually identify items placed on the countertop.
⚡ The system offers a fast, contact-free self-checkout process, reducing wait times in queues.
🛠️ The project requires electronic components and 15 mm thick plywood for construction.
🔧 A load cell is used to measure the weight of objects, mounted at the center of the base.
📷 A camera module and LED strips are installed to identify objects and ensure visibility in low light.
🖌️ The plywood cabinet is assembled, sanded, primed, and painted for an elegant finish.
🔌 The load cell is connected to an amplifier module and a Raspberry Pi for accurate measurements.
💡 The AI model for object detection is trained using 40 images and has a 98.9% accuracy rate.
📦 The final build includes a Python code for the device and a Node.js developed checkout page.

Q & A

What is the purpose of the 'Auto Bear' system presented in the video?
-The 'Auto Bear' system is an AI-powered instant checkout system designed for smaller retail stores, using computer vision and deep learning to visually identify items placed on the countertop for a fast, contact-free self-checkout experience.
What materials are required to build the physical structure of the Auto Bear system?
-The project requires 15 mm thick plywood of specific dimensions, wood screws, a load cell, an amplifier module, a camera module, LED strips, a white acrylic sheet for the countertop, and a small rectangular box for the Raspberry Pi.
Why is sanding necessary before painting the cabinet?
-Sanding is necessary to create an even surface, which is essential for painting. It helps in adhering the paint properly and provides a smooth finish.
How is the load cell integrated into the Auto Bear system?
-The load cell is attached to the center of the base, with positions marked and holes drilled for connections. It is secured in place using nuts and bolts, and an amplifier module is soldered for coupling the load cell to the Raspberry Pi.
What role does the camera module play in the Auto Bear system?
-The camera module, along with artificial intelligence, is used for the visual identification of objects placed on the countertop. It is connected to the Raspberry Pi and positioned beneath the top side of the cabinet.
Why are LED strips used in the Auto Bear system?
-LED strips are used to provide better visibility even in low light conditions, illuminating the items placed on the countertop for accurate identification by the camera module.
How is the load cell calibrated in the Auto Bear system?
-The load cell is calibrated using standard weights or non-weights to ensure accurate measurements of the weight of objects placed on the countertop.
What platform is used for the object detection AI in the Auto Bear system?
-At gimbals is used as the development platform for machine learning on added devices, facilitating the training and deployment of the object detection AI.
How is the dataset for object detection prepared in the Auto Bear system?
-A dataset containing images of the objects to be detected is loaded and labeled, with the process of labeling being automated to some extent by At gimbals to decrease the time required.
What is the reported accuracy of the generated machine learning model for object detection?
-The generated machine learning model has an accuracy of 98.9%, which is considered quite good for object detection tasks.
How is the software for the Auto Bear system developed and where can the code be found?
-The software is written in Python, with the checkout page developed using Node.js. The code can be found in a GitHub repository, the link to which is provided in the video description.

Outlines

00:00

🛒 Introducing Auto Bear: The AI-Powered Instant Checkout System

In this video, we introduce Auto Bear, an AI-driven self-checkout system designed for small retail stores. Using computer vision and deep learning, Auto Bear quickly and contactlessly identifies items placed on its countertop. This system eliminates the need to wait in long queues, offering a fast and efficient checkout experience. The project utilizes 15 mm thick plywood to create an even painting surface, assembling a cabinet with precise placements for components like the load cell and a camera module. The load cell, essential for weight measurement, is securely mounted and connected to a Raspberry Pi. LED strips are installed for better visibility of items, while a white acrylic sheet is used as the countertop for a sleek finish.

05:00

📷 Setting Up the Camera and LED for Visual Identification

The project continues with the installation of a camera module beneath the top side of the cabinet, connected to the Raspberry Pi. This camera, along with artificial intelligence, is crucial for identifying objects on the countertop. Two LED strips are also added to enhance visibility, ensuring items are clearly seen even in low light. The system's elegance is maintained with a white acrylic countertop, while a small rectangular box houses the Raspberry Pi and connections, leading to a polished final setup ready for software integration.

📏 Calibrating the Load Cell for Accurate Weight Measurement

Next, we focus on the calibration of the load cell, which is vital for precise weight measurements of objects on the countertop. The process involves using known weights to adjust the load cell's readings, ensuring accuracy. Once calibrated, the load cell can reliably measure various objects' weights, playing a key role in the self-checkout system's functionality. This step is essential to ensure the system performs consistently and accurately in a real-world retail environment.

🧠 Training the AI for Object Detection

The video then delves into the AI training process for object detection. We start by loading a dataset containing images of specific objects like apples, Lay's chips, and Coke cans. The more images we have, the better the model's accuracy. Each object in the images is manually labeled, a process facilitated by the Gimballs platform, which significantly reduces the time needed for labeling. After labeling, a machine learning model is generated with an impressive accuracy of 98.9%. This model is tested with new images to verify its performance, confirming that the system can correctly identify objects placed on the countertop.

💻 Integrating the Software for Seamless Operation

Finally, the video covers the integration of the software components. The entire code for operating the device is written in Python, with the checkout interface developed using Node.js. Viewers are encouraged to download the code from the provided GitHub repository. With all hardware and software components in place, the Auto Bear system is fully operational. The video concludes by inviting viewers to replicate the project and reach out with any questions, providing contact details and a project link in the description for further guidance.

Mindmap

Keywords

💡Auto Bear

Auto Bear is the name given to the automated checkout system presented in the video. It is a self-service kiosk designed to streamline the checkout process in smaller retail stores. The system uses computer vision and deep learning technologies to identify items placed on the countertop, allowing for a fast and contact-free checkout experience. This is central to the video's theme of showcasing an innovative retail technology.

💡Computer Vision

Computer vision is a field of artificial intelligence that enables computers to interpret and understand the visual world. In the context of the video, computer vision is utilized by Auto Bear to visually identify items placed on the countertop. This technology is crucial for the system's ability to recognize and process the items for checkout, which is a key feature of the system.

💡Deep Learning

Deep learning is a subset of machine learning that involves artificial neural networks with many layers, enabling the model to learn complex patterns in data. In the video, deep learning is applied to train the system to recognize different items. The use of deep learning is essential for the accurate object detection that the Auto Bear system performs during the checkout process.

💡Load Cell

A load cell is a type of force sensor used to measure weight. In the video, the load cell is attached to the base of the Auto Bear system to measure the weight of the objects placed on the countertop. This component is vital for the system to determine the quantity of items and, consequently, their total cost during the checkout.

💡Raspberry Pi

The Raspberry Pi is a series of small single-board computers used for various hardware projects. In the video, it is mentioned as the computing platform that the Auto Bear system uses to process data from the load cell and camera module. It is an integral part of the system's hardware setup.

💡Amplifier Module

An amplifier module is an electronic component that increases the amplitude of a signal. In the context of the video, the amplifier module is used to couple the load cell to the Raspberry Pi, ensuring that the weight measurements are accurately transmitted to the system for processing.

💡Camera Module

A camera module is a component that captures visual images. In the video, the camera module is placed beneath the countertop to capture images of the items for visual identification. It works in conjunction with the deep learning model to recognize the items, which is a fundamental aspect of the checkout process.

💡LED Strips

LED strips are flexible circuits with a series of light-emitting diodes used for illumination. In the video, LED strips are used to provide better visibility for the camera module, especially in low-light conditions. They are an important component for ensuring the accuracy of the object detection process.

💡Calibration

Calibration is the process of adjusting a device or system to ensure it provides accurate and consistent results. In the video, the load cell is calibrated using standard weights to ensure that the weight measurements are precise. This is a critical step for the reliability of the checkout system.

💡Object Detection

Object detection is a computer vision technique that locates and classifies objects within an image. In the video, object detection is performed by the deep learning model to identify the items placed on the countertop. This is a key functionality of the Auto Bear system, allowing it to recognize and process items for checkout.

💡Machine Learning Model

A machine learning model is a system that uses algorithms to learn from and make predictions or decisions based on data. In the video, a machine learning model is trained using a dataset of images to recognize different items. The model's accuracy is crucial for the effectiveness of the object detection in the Auto Bear system.

Highlights

Introduction of Auto Bear, an AI-powered instant checkout system designed for smaller retail stores.

Utilizes computer vision and deep learning to visually identify items on the countertop.

Provides a fast, contact-free self-checkout system, reducing the need to wait in long queues.

Electronic components required for the project are listed.

Use of 15 mm thick plywood for construction, with detailed sanding for a smooth surface.

Attachment of the load cell to the center of the base for measuring object weight.

Creation of a slit for LED connection wires and camera cable to connect to the Raspberry Pi.

Construction of a cabinet using plywood parts and wood screws, followed by priming and painting.

Mounting the load cell and securing it with nuts, bolts, and washers.

Placement of the amplifier module near the load cell and soldering necessary connections.

Use of a camera module with AI for visual identification of objects on the countertop.

Installation of LED strips for better visibility in low light conditions.

Use of a white acrylic sheet as a countertop for a neat look.

Mounting the Raspberry Pi and connecting all components to it.

Calibration of the load cell using known weights to ensure accurate measurements.

Training of the object detection AI using images of apples, lace, and coke with the Edgimble platform.

Labeling objects in images for training, enhancing the model's accuracy.

Generated machine learning model achieves 98.9% accuracy.

Live classification testing confirms the model's ability to accurately identify objects.

Code for the device is written in Python, with the checkout page developed using Node.js.

Project details and code are available on GitHub.

Encouragement for viewers to replicate the project and contact for any doubts or questions.