SMART DETECTION OF TRAFFIC VIOLATIONS WITH REAL-TIME HELMET AND LICENSE PLATE RECOGNITION USING AI

Abstract

SMART DETECTION OF TRAFFIC VIOLATIONS WITH REAL-TIME HELMET AND LICENSE PLATE RECOGNITION USING Al TECHNIQUES ABSTRACT In 2024, over 50% of road fatalities involved motorcyclists not wearing helmets, underscoring the urgent need for effective, safety measures. Our research presents a cutting-edge solution by integrating helmet detection and license plate recognition using advanced machine learning techniques. Leveraging Convolutional Neural Networks (CNN) and YOLOv3 (You Only Look Once), the system proposed to accurately detect helmet usage and identify motorcycles and license plates in real-time video streams. The YOLOv3 model processes video data, outputting bounding boxes and confidence scores for detected objects, while non-maximum suppression refines these results. The CNN model then analyses regions of interest to confirm helmet usage, labelling motorcycles as "helmet" or "no helmet. Our innovative approach promises to enhance road safety by ensuring real-time helmet compliance, significantly reducing motorcycle-related accidents and fatalities.

Specification

FORM - 2
THE PATENTS ACT, 1970
(39 OF 1970)
&
THE PATENTS RULES, 2003
COMPLETE SPECIFICATION
(see Section 10 & rule 13)

1. TITLE OF TI IE INVENTION : SMART DETECTION OF TRAFFIC VIOLATIONS WITH
REAL-TIME HELMET AND LICENSE PLATE RECOGNITION USING Al TECHNIQUES
2. APPLICANT:
Saveetha Institute of Medical and
Technical Sciences
NAME NATIONALITY ADDRESS
INDIAN Saveetha Nagar, Thandalam,
Chennai - 602 105,Tamil Nadu,
India
3. PREAMBLE TO THE DESCRIPTION:
26-Nov-2024/140479/202441092027/Form 2(Title Page)
The following specification describes the invention and how it is to be performed.
4. COMPLETE SPECIFICATION
The following specification particularly describes the invention and the manner in which it is tobe
performed. ....................... Separate sheet is attached......................................
5. DESCRIPTION
Separate sheet is attached______
6. CLAIMS
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DATE:
SIGNATURE:
NAME
1. ABSTRACT OF THE INVENTION
Separate sheet is attached-
Dr. B.HAWiESH
PRINCIPAL
SIMATS ENGINEERING
SAVEETHA INSTITUTE OF MEDICAL AND
Note:- TECHNIAL SCIENCES
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Bank : INDUSIND BANK
Branch : NUNGAMBAKKAM
( SAVEETHA UNIVERSITY)
DD No : 402666
DD Date: 16/11/2024
DD Amount: 8,900/-

SMART DETECTION OF TRAFFIC VIOLATIONS WITH REAL-TIME HELMET AND
LICENSE PLATE RECOGNITION USING Al TECHNIQUES
26-Nov-2024/140479/202441092027/Form 2(Title Page)
PREAMBLE TO THE DESCRPT1ON
FIELD OF INVENTION
This invention focuses on innovative advancements in traffic management and road safety systems. It leverages machine learning techniques, including Convolutional Neural Networks (CNN) and YOLOv3, for real-time detection of traffic violations such as helmet compliance and license plate recognition. The goal is to improve road safety, reduce accidents, and enhance the enforcement of traffic laws, ensuring a safer and more efficient transportation environment.

BACKGROUND OF THE INVENTION
Two-wheelers are a common mode of transportation due to their affordability and accessibility. However, the increase in motorcycle accidents, often due to reckless riding and the neglect of helmet use, poses a significant safety concern. Not wearing helmets increases the risk of head injuries during accidents. This issue has broader implications, affecting public health, healthcare costs, and community well-being. Addressing this problem is vital. The failure of many motorcyclists to wear helmets increases the likelihood of head trauma in accidents. Although many countries have implemented helmet-wearing laws, enforcement remains challenging. Current systems for enforcing helmet compliance are inefficient and costly. This project aims to develop a real-time system for identifying non-helmeted motorcycle riders using advanced technologies such as the YOLOv3 model and a custom CNN model. The system seeks to enhance road safety and increase helmet compliance by automating the detection process.

SUMMARY OF THE INVENTION
The invention introduces an advanced system for real-time detection of traffic violations, focusing on helmet compliance and license plate recognition using machine learning techniques. Integrating CNN for helmet detection and YOLOv3 for recognizing motorcycles and license plates, the system automates the enforcement of helmet-wearing laws. This innovative approach enhances road safety by providing accurate, real-time detection capabilities, reducing the need for manual checks by traffic police. Key components include video processing, object detection, and classification, facilitated by advanced machine learning models. This invention offers significant improvements over traditional methods, contributing to enhanced road safety, reduced accidents, and improved traffic law enforcement.

SMART DETECTION OF TRAFFIC VIOLATIONS WITH REAL-TIME HELMET AND LICENSE PLATE RECOGNITION USING Al TECHNIQUES

COMPLETE SPECIFICATION
Specifications
• Processing: The system starts by reading and resizing video footage for analysis.
• YOLOv3: This model detects motorcycles and license plates, providing bounding boxes and confidence scores.
• CNN Model: Analyzes regions of interest (ROls) from detected motorcycles to classify helmet usage.
• Secure Database: Stores processed video data and detection results for further analysis and monitoring.
• Implementation: The system uses Python, TensorFlow, Keras, OpenCV, and custom scripts.
• Benefits: The system enhances road safety by ensuring helmet compliance, reducing accidents, and improving traffic law enforcement efficiency.

SMART DETECTION OF TRAFFIC VIOLATIONS WITH REAL-TIME HELMET AND LICENSE PLATE RECOGNITION USING Al TECHNIQUES

DESCRIPTION
The advanced system for real-time detection of traffic violations using helmet and license plate recognition with machine learning techniques includes the following components:

Custom Dataset: Diverse video footage capturing various scenarios of motorcycle riders with and without helmets.

YOLOv3 Framework: Detects motorcycles and license plates, providing bounding boxes and confidence scores.

CNN Model: Analyses ROIs from detected motorcycles to classify helmet usage, assigning probability scores for helmet or no helmet.

Video Processing: Involves reading, resizing, and converting video data for analysis, followed by object detection and classification.

Data Analysis: Processes video data to extract relevant features, ensuring accurate and efficient detection of traffic violations.

Benefits:
Enhanced Road Safety: Accurate detection of helmet compliance reduces the risk of accidents and injuries.

Automated Enforcement: Reduces the workload on traffic police by automating the detection of traffic violations.

Efficiency: Real-time detection capabilities improve the efficiency of traffic law enforcement.

Public Trust: The system's transparency and reliability enhance public trust in traffic management and road safety measures.

SMART DETECTION OF TRAFFIC VIOLATIONS WITH REAL-TIME HELMET AND LICENSE PLATE RECOGNITION USING Al TECHNIQUES


CLAIM
We Claim
1. Claim: The system accurately identifies motorcycles, license plates, and helmet compliance under various conditions.

2. Claim: Enhances road safety by reducing the risk of accidents and injuries through precise real-time detection ofTielmet usage.

3. Claim: Uses a comprehensive and diverse custom dataset ensuring robustness and reliability in real-world scenarios, improving detection accuracy and performance.

4. Claim: Combines YOLOv3 for object detection and CNN for helmet classification, providing an effective solution fortraffic law enforcement.

5. Claim: Designed to be scalable and integrable into various traffic management and road safety systems globally.

6. Claim: Advanced preprocessing methods ensure consistent and high-quality data preparation, enhancing the model's performance and accuracy.

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