Road Sign Recognition with Fuzzy Adaptive Pre-Processing Models
A road sign recognition system based on adaptive image pre-processing models using two fuzzy inference schemes has been proposed. The first fuzzy inference scheme is to check the changes of the light illumination and rich red color of a frame image by the checking areas. The other is to check the va...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2012-05-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-8220/12/5/6415 |
| _version_ | 1811262990559739904 |
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| author | Ming-Shi Wang Chien-Chuan Lin |
| author_facet | Ming-Shi Wang Chien-Chuan Lin |
| author_sort | Ming-Shi Wang |
| collection | DOAJ |
| description | A road sign recognition system based on adaptive image pre-processing models using two fuzzy inference schemes has been proposed. The first fuzzy inference scheme is to check the changes of the light illumination and rich red color of a frame image by the checking areas. The other is to check the variance of vehicle’s speed and angle of steering wheel to select an adaptive size and position of the detection area. The Adaboost classifier was employed to detect the road sign candidates from an image and the support vector machine technique was employed to recognize the content of the road sign candidates. The prohibitory and warning road traffic signs are the processing targets in this research. The detection rate in the detection phase is 97.42%. In the recognition phase, the recognition rate is 93.04%. The total accuracy rate of the system is 92.47%. For video sequences, the best accuracy rate is 90.54%, and the average accuracy rate is 80.17%. The average computing time is 51.86 milliseconds per frame. The proposed system can not only overcome low illumination and rich red color around the road sign problems but also offer high detection rates and high computing performance. |
| first_indexed | 2024-04-12T19:36:56Z |
| format | Article |
| id | doaj.art-da801997fbbc43e9b9f54eea1a9ffd5e |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-12T19:36:56Z |
| publishDate | 2012-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-da801997fbbc43e9b9f54eea1a9ffd5e2022-12-22T03:19:12ZengMDPI AGSensors1424-82202012-05-011256415643310.3390/s120506415Road Sign Recognition with Fuzzy Adaptive Pre-Processing ModelsMing-Shi WangChien-Chuan LinA road sign recognition system based on adaptive image pre-processing models using two fuzzy inference schemes has been proposed. The first fuzzy inference scheme is to check the changes of the light illumination and rich red color of a frame image by the checking areas. The other is to check the variance of vehicle’s speed and angle of steering wheel to select an adaptive size and position of the detection area. The Adaboost classifier was employed to detect the road sign candidates from an image and the support vector machine technique was employed to recognize the content of the road sign candidates. The prohibitory and warning road traffic signs are the processing targets in this research. The detection rate in the detection phase is 97.42%. In the recognition phase, the recognition rate is 93.04%. The total accuracy rate of the system is 92.47%. For video sequences, the best accuracy rate is 90.54%, and the average accuracy rate is 80.17%. The average computing time is 51.86 milliseconds per frame. The proposed system can not only overcome low illumination and rich red color around the road sign problems but also offer high detection rates and high computing performance.http://www.mdpi.com/1424-8220/12/5/6415road sign recognitionfuzzy inferenceAdaboost classifiersupport vector machine |
| spellingShingle | Ming-Shi Wang Chien-Chuan Lin Road Sign Recognition with Fuzzy Adaptive Pre-Processing Models Sensors road sign recognition fuzzy inference Adaboost classifier support vector machine |
| title | Road Sign Recognition with Fuzzy Adaptive Pre-Processing Models |
| title_full | Road Sign Recognition with Fuzzy Adaptive Pre-Processing Models |
| title_fullStr | Road Sign Recognition with Fuzzy Adaptive Pre-Processing Models |
| title_full_unstemmed | Road Sign Recognition with Fuzzy Adaptive Pre-Processing Models |
| title_short | Road Sign Recognition with Fuzzy Adaptive Pre-Processing Models |
| title_sort | road sign recognition with fuzzy adaptive pre processing models |
| topic | road sign recognition fuzzy inference Adaboost classifier support vector machine |
| url | http://www.mdpi.com/1424-8220/12/5/6415 |
| work_keys_str_mv | AT mingshiwang roadsignrecognitionwithfuzzyadaptivepreprocessingmodels AT chienchuanlin roadsignrecognitionwithfuzzyadaptivepreprocessingmodels |