基于颜色特征和SVM的交通标志检测

为提高道路交通标志识别系统的实时性和准确度，提出一种改进的RGB空间颜色检测和SVM相结合的交通标志检测算法。首先使用直方图均衡化和Gabor滤波相结合的方法进行图像增强，突出目标颜色；然后使用改进的RGB空间颜色检测方法初步提取并切割出候选标志区域；最后使用HOG特征训练SVM分类器，对候选标志进行精确检测并判断其形状。在检测精度和检测用时2方面进行对比试验，其结果表明，本文算法的检测用时较短，误检率和错检率都较低。该算法能对亮度较低的图像进行有效处理，对旋转、部分遮挡等多种情况也有较优的稳定性和准确性，适用于复杂背景下的标志检测。

Traffic Sign Detection Based on Color Feature and SVM

Focused on the problem of time consumption and accuracy of traffic sign recognition system, an efficient algorithm for traffic sign detection based on improved color detection in RGB color space combined with support vector machine is presented. Firstly, the histogram equalization and Gabor filter are applied for image enhancement to highlight the image color. Secondly, an improved color detection method is proposed to select the coarse candidate areas. Thirdly, a SVM classifier is trained using HOG feature for further detection and shape judgment. Comperative tests are done about detection accuracy and detection time consumption. The experimental results show that this algorithm testing time is short, error detection rate and false detection rate are low. The algorithm can detect signs quickly and determine the shape. It can also deal with various cases such as images with low brightness, rotation and partial occlusion with high accuracy. Compared with color enhancement algorithm, this method can achieve better accuracy and has less time consumption.