AutoBill - An AI Powered Instant Checkout System | Edge Impulse | Raspberry Pi | Coders Cafe
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
🛒 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.
📷 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
💡Computer Vision
💡Deep Learning
💡Load Cell
💡Raspberry Pi
💡Amplifier Module
💡Camera Module
💡LED Strips
💡Calibration
💡Object Detection
💡Machine Learning Model
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.
Transcripts
[Music]
in this video we present you auto bear
an a powered instant checkout system
which is specifically designed for
smaller retail stores auto produces
computer vision and deep learning to
visually identify the items which is
placed on the countertop it's an
incredibly fast contact free
self-checkout system so don't waste your
time by waiting in long queues just
place your things on the countertop and
check out instantly so enough
description for now so let's get start
the video
these are the electronic components
required for the project
in this project we use 15 mm thick
plywoods of shown dimensions
sani helps to create an even surface
which is an essential requirement for
painting so we are starting with the
fine grits and ending with the very fine
grits
we need to attach the load cell to the
center of the base
for this mark the positions accordingly
and drill three holes
two of them for connecting the load cell
and other for taking out connections
from the load cell
[Music]
also we need a thin wired slit for
taking led connection wires and camera
cable to the raspberry pi
so let's make a slit by drilling
consecutive holes
connect all the plywood parts using
normal wood screws to form a cabinet
so here is our cabinet and all layers of
paint are still visible
let's cover them up with a coat of paint
firstly prime the surfaces with the two
coats of normal wood primer sanding well
between each cord
once it's done apply a few coats of
white satin finish paint for an elegant
look
the load cell is used for measuring the
weight of the objects placed on the
countertop attached to it mount the load
cell to the base using nets bolts along
with the proper washers and tighten them
up to secure the load cell in
position the amplify module is an
essential component for coupling the
load cell to the raspberry pi now place
the amplifier module near the load cell
and solder all the for incoming and
outgoing connections
refer to the circuit diagram given in
the description in case of any doubts
once the soldering is done pass the
wires through the hole that we made in
purpose
a camera module along with artificial
intelligence is used for the visual
identification of objects placed on the
ground on top
stick the camera module beneath the top
side of the cabinet and connect it to
the raspberry pi using the camera cable
for better visibility even in low light
conditions we have used the two led
strips that are capable of illuminating
the things placed on the countertop cut
the led strips in decide length and fix
them on either side of the camera module
a white acrylic sheet can be used as a
countertop which can give a neat look to
the device
[Music]
attach a small rectangular box on the
side of the cabinet where we can place
our raspberry pi and all the connections
are made to it with all of this done the
final output will look like this
let's move on to the software part
to ensure that the load cell
measurements are accurate we need to
calibrate them with either standard
weights or non weights let's use the
caucas non-weight and calibrate the load
cell
once the calibration is done properly
the load cell can accurately measure the
weight of objects placed on the
countertop
for the object detection ai we have used
at gimbals which is a leading
development platform for machine
learning on added devices
read more about them in the description
now let's move on to the model training
part
to start with let's load the data set
which contains images of the object that
are to be detected
in this project we have collected 40
images of apple lace and coke the more
images we have the better will be
accuracy
once the dataset is loaded we have to
label the objects in each of the images
labeling is the process of identifying
images from an image and adding
necessary information about them so that
the mission can learn from them
labeling is a time consuming manual
process but at gimbals decreases the
leveling time to a great extent by
automatically identifying objects from
the
image
[Music]
ensure the leveling is correct for all
the objects in each of the images after
the labeling is complete let's generate
the machine learning model follow the
steps carefully and generate the model
which can be used on the raspberry pi
[Music]
[Music]
so
the generated model has an accuracy of
98.9 percentage which is pretty good
just to check the accuracy of the model
we have collected some images that were
not used for training move on to the
live classification and load the sample
yeah it's working the generated model
has identified the object in the image
download the model and let's go to
coding the entire code for the device is
written in python and the checkout page
is developed using node.js grab the code
from the github repository whose link is
given in the description
so our build is complete
[Music]
[Music]
if you have any doubts put them in the
comment section or feel free to contact
us through our email if you are really
interested in replicating this project
don't forget to check out the project
link given in the description
so see in the next video till then stay
tuned
[Music]
you
Ver Más Videos Relacionados
Face Recognition With Raspberry Pi + OpenCV + Python
Object Detection Using OpenCV Python | Object Detection OpenCV Tutorial | Simplilearn
Shopping in the future retail store.
FIFISH PRO W6 - Industrial-Grade Underwater ROV Platform FIFISH PRO W6
Vision Assistant for Visually Impaired People | College Mini Project
Welcome to the Hugging Face course
5.0 / 5 (0 votes)