The document presents an overview of a project by the Tech Pulse robotics team from Adhiyamaan College of Engineering that utilizes smart vision technology for automated quality control in product inspection. The system employs advanced imaging and machine learning to identify, count, and assess the quality of products in real time, particularly focusing on freshness of fruits and vegetables, barcode scanning for product information, and alerting for expired items. Future enhancements include robotics for inspection and smart packaging solutions for improved monitoring.

2. Team Intro
TITLE: Smart Vision Technology Quality Control
TRACK NAME: Robotics
TEAM NAME: Tech Pulse
TEAM MEMBERS: GRISHMA S
(Team leader)
DARSHAN N
(Team member)
DHANUSH H
(Team member)
COLLEGE: ADHIYAMAAN COLLEGE OF ENGINEERING
(AUTONOMOUS), HOSUR
DATE: 19/10/2024

3. Executive Summary:
OVERVIEW:
Smart vision technology with advanced imaging systems
and algorithms to capture and analyze visual information.
In the context of quantity and quality testing, it helps
automate the quality inspection process by identifying a
product, its quantity and any defects or quality attributes.
IDEA BEHIND THE SOLUTION:
1. Our project streamlines product inspection by
automatically identifying and counting items in
real time.
2. It utilizes advanced technology to assess the
freshness of fruits and vegetables and monitor their
expiration dates.
3. By scanning barcodes, the system retrieves vital
product details, including brand, ingredients, and
nutritional information.
4. The system promptly alerts the team if any product
is expired or compromised, facilitating swift
action.
5. The data analyzed to enhance inventory
management and optimize operational efficiency.

4. Technical Approach:
TECHNOLOGY STACK:
 PROGRAMMING LANGUAGE: Python
 MODELS: YOLOv8, Convolutional Neural Network (CNN)
 FRAMEWORKS: TensorFlow/Keras
 LIBRARIES: OpenCV, PyTorch
 TOOL: EasyOCR, Matplotlib, Plotly
 API: Barcode Lookup API
 DATABASE: MySQL
PROPOSED SOLUTION:
The proposed system incorporates advanced machine learning and computer
vision techniques for real-time product monitoring and quality control:
Counting Specified Objects (YOLOv8):
• Detects and counts specific objects in real time, excluding irrelevant
classes like humans.
• Uses a predefined polygon zone to focus counting within a designated
area.
Freshness Prediction (CNN):
• Analyzes live video feed to classify the freshness of fruits and vegetables
as either "Healthy" or "Rotten" using a pre-trained CNN model.
• Utilizes smoothing to stabilize predictions across frames.
OCR Expiry Date Detection (EasyOCR):
• Extracts expiry dates from product labels using EasyOCR.
• Triggers alerts if the product is expired.
Brand and Product Details Extraction (Barcode & API):
• Detects and decodes barcodes from product packaging and retrieves
detailed information from an external API.
• Fetches product details such as brand, ingredients, nutritional info, and
packaging details.
Data Analytics:
• The system generates performance analytics and visual graphs from the
stored data, allowing for trend analysis and optimized decision-making.
HARDWARE SPECIFICATIONS:
 Camera with real-time video capture for product analysis and
barcode scanning.
 GPU for efficient deep learning computations with YOLOv8 and
CNN models.
 High-performance storage to manage large datasets for object
detection and freshness classification.
FUTURE SCOPE:
 Robotics for Automated Inspection: Incorporate robotic systems for
automated handling and inspection of products, significantly speeding
up quality checks in warehouses and distribution centers.
 Smart Packaging Solutions: Develop smart packaging that uses
embedded sensors to provide real-time freshness and quality data
directly to the monitoring system.

5. ITEMS USED TO TRAIN MODEL:
Object detection pretrained model: yolov8l.pt
Freshness detection dataset: fruits and vegetables freshness
Brand and details extraction: BARCODE LOOKUPAPI
CODE:
Object detection and count of specified objects: https://github.com/GrishmaSuresh/object-detection-including-count
Brand and product details extraction: https://github.com/GrishmaSuresh/product_details
Expiry date validation: https://github.com/GrishmaSuresh/expiry_date
Freshness detection: https://github.com/GrishmaSuresh/freshness_detection
VIDEO SIMULATION:

6. Thank You!