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Published on: March 17, 2023
Complete vision-based traffic sign recognition supported by an I2V communication system.
Miguel A García-Garrido1, Manuel Ocaña1, David F Llorca2
1Electronics Department, Polytechnic School, University of Alcalá, Madrid 28871, Spain.
Sensors (Basel, Switzerland)
|March 23, 2012
Summary
This study introduces an advanced traffic sign recognition system using vision sensors and Support Vector Machines (SVM). It achieves high detection and recognition rates for road signs in real-time driving conditions.
Area of Science:
- Computer Vision
- Artificial Intelligence
- Automotive Safety
Background:
- Traffic sign recognition is crucial for intelligent transportation systems.
- Existing systems face challenges in accuracy and real-time performance.
Purpose of the Study:
- To develop a comprehensive traffic sign recognition system for vehicles.
- To improve the accuracy and efficiency of detecting and recognizing road signs.
Main Methods:
- Utilized a vision sensor with restricted Hough transform for sign detection.
- Employed Support Vector Machines (SVM) for sign recognition.
- Integrated infrastructure-to-vehicle (I2V) communication, stereo vision, CAN Bus, and GPS for accurate sign localization and filtering.
Main Results:
- Achieved an average detection rate exceeding 95%.
- Attained an average recognition rate of approximately 93%.
- Demonstrated real-time performance with an average runtime of 35 ms.
Conclusions:
- The developed system offers robust and efficient traffic sign recognition.
- The integration of multiple sensors and I2V communication enhances system reliability.
- The system is suitable for real-world driving conditions, day and night.
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