Traffic Sign Detection and Recognition

Abstract

Traffic sign recognition and detection is a critical component of intelligent transportation systems aimed at enhancing road safety and driver assistance. This technology involves the use of computer vision and machine learning algorithms to detect and classify various road signs such as stop signs, speed limits, and warning signsinreal-time.By utilizingcamerasorsensorsmounted on vehicles, the system identifies these signs and provides timely alerts to the driver. The addition of voice message alerts further improves driver awareness, offering immediatenotificationslike“Speedlimit50km/h”or“Stop sign ahead.” technique for detecting traffic signs that in reducing human errors but also enhances the overall driving experience by ensuring that drivers are well- informed about changing road conditions, contributing to safer roadways navigation. This report examines the development of a technique for detecting traffic signs that utilizing machine learning (ML) and as well as artificial intelligence (AI) techniques. The system integrates various technologies, including Flutter for user interface development, OpenCV for image processing, Large Language Models (LLM) for enhanced decision-making, networking tunnels for real-time data transmission, and Text- to-Speech (TTS) for auditory feedback. The implementation aims to to increase the precision and efficiency of traffic sign recognition, contributing to the advancement of intelligent transportation systems.

Introduction

Traffic Sign Detection is a critical component of modern intelligent transportation systems, enhancing road safety and traffic management, especially with the rise of Advanced Driver Assistance Systems (ADAS) and autonomous vehicles. Accurate, real-time recognition of traffic signs is essential to prevent accidents and ensure smooth traffic flow.

Challenges with Traditional Methods:
Conventional rule-based image processing struggles with real-world issues like varying lighting, occlusions, motion blur, and diverse sign designs, limiting their reliability.

Proposed AI-Powered System:
The project introduces a real-time traffic sign detection system using a Convolutional Neural Network (CNN) for robust image classification under varied environmental conditions. OpenCV is used for image preprocessing, while a Flutter-based mobile app offers a user-friendly interface. Secure networking tunnels ensure data integrity, and Text-to-Speech (TTS) technology provides auditory alerts, enhancing accessibility for visually impaired drivers. Data augmentation techniques improve model generalization.

Literature Review Highlights:

• CNNs have shown high accuracy in traffic sign recognition.

• Machine learning models like SVM and Random Forest require manual feature extraction and have limitations.

• Sensor fusion (camera + LiDAR), IoT integration, cloud systems, and federated learning improve detection accuracy, privacy, and adaptability.

• TTS alerts enhance accessibility, and anomaly detection (e.g., damaged signs) is an emerging focus.

System Modules:

• Data Collection: Captures real-time images/video with GPS integration.

• Image Preprocessing: Enhances image quality for better detection.

• Detection and Classification: CNN detects and categorizes signs (speed limits, warnings, etc.).

• TTS Module: Converts detected signs into voice alerts.

• Networking & Cloud Storage: Secures data transmission and supports V2V communication.

• Integration & Deployment: Optimizes system for ADAS and autonomous vehicle use with edge computing for low latency.

System Architecture:
The system captures images, preprocesses them, applies CNN-based detection, analyzes context, stores data securely, and provides outputs via UI and TTS. This makes it suitable for autonomous navigation, driver assistance, and smart surveillance.

Results:
The system successfully detects various signs like “No U-Turn,” “Pedestrian Crossing Ahead,” “Sharp Left Turn Ahead,” and “Stop Sign,” demonstrating practical application for traffic safety.

Conclusion

Using machine learning (ML) and artificial intelligence (AI) approaches, the proposed Traffic Sign Detection System effectively recognizes and categorizes traffic signs in real time. A smooth user experience is guaranteed by the combination of Flutter for user interface, OpenCV for image processing, Large Language Models (LLMs) for contextual analysis,networkingtunnelsfordatatransport,andText-to- Speech (TTS) for audio output. By using edge computing, asynchronous processing, and model quantization, the system operates effectively on mobile devices, cutting down on inference time while preserving user interface responsiveness. The system\'s low inference time (~50-100 ms per frame), high detection accuracy (~90% or greater), and efficient power consumption make it a good fit for autonomous cars and Advanced Driver Assistance Systems (ADAS).

References

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Copyright

Copyright © 2025 Nagarjuna G R, Busetty Akshitha, Palagiri Jahnavi, Shashitha S M, Suragani Saranya Goud. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.