圆形交通标志牌识别系统的仿生设计

提出了一种基于对数极坐标变换与Zernike矩相结合的交通标志牌识别算法。首先,在HSI空间采用直方图均衡化来提高图像的对比度,随后以颜色为特征对交通标志牌进行检测,分割并且对存在过度分割的区域进行合并,接着以形状为特征对检测到的候选区域进行筛选以及归一化;然后结合对数极坐标变换以及Zernike矩来提取图像的特征;最后通过支持向量机(support vector machine,SVM)分类器来实现目标识别。实验结果显示,交通标志牌检测正确率达到了94.71%,识别率达到了85%,表明该算法可有效地识别出不同光照条件下发生变形,缩放及旋转后的交通标志牌。

Bionic design of circular traffic signs recognition system

In this paper, a traffic sign recognition algorithm based on log-polar transformation and Zernike moment was presented.First, to improve the image contrast, histogram equalization was performed in HSI color space towards the image captured from complex natural environment.After that, traffic sign was detected by color, and segmentation and region merging was carried out.Next, it's followed by screening by shape and subsequent normalization.Then, images' Zernike moment was computed combining log-polar transformation.Lastly, SVM classifier was used to recognize the object.The experiment result shows 94.71% detection accuracy and 85% recognition accuracy, which demonstrates that the traffic sign recognition system can effectively recognize the distortional, scaling or rotated traffic signs.