| 张震,程伟伟,吴磊,张斌.基于不变矩和SVM的圆形交通标志识别方法研究[J].电子测量与仪器学报,2017,31(5):773-779 |
| 基于不变矩和SVM的圆形交通标志识别方法研究 |
| Study on circular traffic signs recognition method based on invariant moments and SVM |
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| DOI：10.13382/j.jemi.2017.05.017 |
| 中文关键词: 交通标志识别 Hu矩 Zernike矩 支持向量机 |
| 英文关键词:traffic signs recognition Hu moments Zernike moments support vector machine |
| 基金项目: |
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| Author | Institution |
| Zhang Zhen | School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200072, China |
| Cheng Weiwei | School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200072, China |
| Wu Lei | School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200072, China |
| Zhang Bin | Shandong Luneng Technology Co. Ltd., Ji’nan 250002, China |
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| 中文摘要: |
| 针对道路交通标志的自动识别问题，通过不变矩和支持向量机（SVM）方法对圆形标志图像识别方法进行研究。首先根据交通标志的颜色和形状信息对采集到的原始图像进行颜色分割、形态学去噪和形状检测等处理，获得图像中包含交通标志的区域。然后分别对标志图像进行Hu矩和Zernike矩的特征值提取，将特征值输入SVM中进行训练并采用网格搜索法对SVM进行参数优化，最后使用优化后的支持向量机方法实现交通标志的识别。实验表明，与现有的其他交通标志识别算法相比，采用高阶Zernike矩与优化后SVM的识别方法有更好的识别效果。 |
| 英文摘要: |
| Aiming at the problem of automatic traffic signs recognition, the method of circle signs image recognition based on invariant moments and support vector machine (SVM) is studied in this paper. Firstly, according to the color and shape information of traffic sign, the original image is processed by color segmentation, morphological denoising and shape detection. Then, Hu and Zernike invariant moments of the images are extracted to establish the corresponding feature data set, and the data set is input into SVM and the grid search technique is used to optimize the parameters of SVM. Finally, the traffic signs are recognized in the trained SVM classifier. The experimental results show that compared with other existing traffic signs recognition algorithms, the high order Zernike moments and the optimized SVM have better recognition results. |
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