Department of Engineering Science, National Cheng Kung University Taiwan, No.1, University Road, Tainan City 701, Taiwan.
Sensors (Basel). 2012;12(5):6415-33. doi: 10.3390/s120506415. Epub 2012 May 15.
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.
已提出一种基于自适应图像预处理模型的道路标志识别系统，该模型使用两种模糊推理方案。第一种模糊推理方案是通过检查区域检查帧图像的光照变化和丰富的红色。另一种是检查车辆速度的变化和转向盘的角度，以选择自适应的检测区域的大小和位置。AdaBoost 分类器用于从图像中检测道路标志候选，支持向量机技术用于识别道路标志候选的内容。本研究的处理目标是禁止和警告道路交通标志。在检测阶段的检测率为 97.42%。在识别阶段，识别率为 93.04%。系统的总准确率为 92.47%。对于视频序列，最佳准确率为 90.54%，平均准确率为 80.17%。平均计算时间为每帧 51.86 毫秒。所提出的系统不仅可以克服道路标志周围光照不足和丰富红色的问题，而且还可以提供高检测率和高计算性能。
