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Road sign recognition with fuzzy adaptive pre-processing models
Chien-Chuan Lin1, Ming-Shi Wang
1Department of Engineering Science, National Cheng Kung University Taiwan, No.1, University Road, Tainan City 701, Taiwan. n9893106@mail.ncku.edu.tw
Sensors (Basel, Switzerland)
|July 11, 2012
Summary
This study introduces an adaptive road sign recognition system using fuzzy logic for improved image pre-processing. The system achieves high detection and recognition rates, even in challenging lighting conditions.
Area of Science:
- Computer Vision
- Artificial Intelligence
- Machine Learning
Background:
- Road sign recognition is crucial for intelligent transportation systems.
- Existing systems struggle with variable illumination and color challenges.
Purpose of the Study:
- To develop an adaptive road sign recognition system.
- To enhance detection and recognition accuracy under adverse conditions.
Main Methods:
- Adaptive image pre-processing using two fuzzy inference schemes.
- Adaboost classifier for candidate detection.
- Support Vector Machine (SVM) for content recognition.
Main Results:
- Detection rate: 97.42%
- Recognition rate: 93.04%
- Overall accuracy: 92.47%
- Average computing time: 51.86 ms/frame
Conclusions:
- The proposed system effectively handles low illumination and color issues.
- Achieves high detection and recognition performance.
- Demonstrates efficient real-time processing capabilities.
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