Research And Implementation Of Urban Traffic Sign Recognition System

With the presence of some bad driving habits, such as answering the phone while driving, and chatting with other people in the car, the driver's attention is easy to be scattered. Thus, the phenomenon of driver's ignoring traffic signs is becoming more and more common. Bad weather and other bad driving conditions sometimes cause drivers not to be vigilant all the time, and thus, they often ignore important traffic signs along the road. It is well known that traffic accidents can occur when drivers miss or ignore some important traffic signs. As the traffic becomes more and more crowded and there are more and more traffic signs, it is necessary to design a system that can help drivers to identify traffic signs in order to reduce traffic accident.On the basis of researching and analyzing existing traffic sign recognition algorithms, this thesis takes such traffic signs lying in a real scenarios with low illumination as the main research object, and presents such an algorithm that is suitable for all-weather road traffic sign detection and recognition. After the algorithm design verification is completed, the identification system is transplanted to and implemented on the hardware platform.In view of the low visibility road environment, the thesis, in the stage before the traffic sign detection, properly uses the Retinex algorithm with color restoration to preprocess the images for enhancement. This is really a good preprocessing algorithm for improving system identification accuracy. In the stage of traffic sign detection, a method based on the combination of color space and shape characteristics is adopted. The segmentation results based on the normalized RGB and the HSV color space are compared. In order to avoid mutual interference, the thesis proposes that the interesting color components after segmentation are stored separately. Additionally, the image with color components below a threshold has no subsequent processing, which can improve the system efficiency. In the stage of shape detection, in order to improve the recognition accuracy, different methods are used to detect traffic signs with different shapes, and finally candidate regions of traffic signs are extracted. In the stage of road traffic sign recognition, in order to achieve good recognition effect and generalization ability, Hu invariant moments are used for feature extraction object in this thesis. Multi class classifier with appropriate classifier parameters based on support vector machine is successfully designed.Finally, Cubieboard4 is chosen as the hardware platform of system implementation. Because of Cubieboard4's higher frequency and plenty of memory, software system can be transplanted into the embedded platform using GCC by direct compiling for this system. According to the experimental results, the system can achieve the real-time requirements, and has high recognition accuracy in low visibility, which shows the feasibility of the system.