Abstract: Intelligent vehicles need to use many characteristic parameters in unstructured road detection, which makes the feature fusion recognition difficult and computation complex, and the similarity of some road area and background may produce the mistake distinguishment and judgement of road identification. In order to solve the problems, an unstructured road detection method based on improved region growing with Principal Component Analysis-Support Vector Machine (PCA-SVM) rule was proposed. Firstly, the complex characteristic parameters such as color and texture of unstructured road were extracted, and then the PCA was used to reduce the dimension of the extracted characteristic information. The SVM trained with the primary characteristics reduced by PCA was used to be the classifier of the complex road cells. The priori knowledge such as the location of road, the initial cell and the characteristics of road boundary cells were used to improve the region growing method, and the classifier was used to decide the way of growing in cell growth for eliminating miscalculation area. The test results of actual roads show that, the proposed method has good adaptability and robustness, and can identify the unstructured road area effectively. The comparison results show that, compared with the traditional algorithm, the proposed method can shorten the calculation time by more than half through cutting characteristics from ten dimensions to three dimensions in ensuring the accuracy at the same time. The proposed method can also eliminate the 10% of miscalculation areas made by some similar areas of road and background for the traditional algorithm. The proposed method can provide a feasible way to shorten the recognition time and eliminate background interference in local path planning and navigation based on vision in the wild environment.

Key words: Support Vector Machine (SVM), Principal Component Analysis (PCA), region growing, unstructured road detection, intelligent vehicle

摘要： 针对智能车辆在非结构化道路识别中需要采用众多的特征参数，增加了特征融合识别难度与计算复杂度，并且部分背景与道路区域存在相似性会产生道路识别的误分、误判的问题，提出了一种基于主成分分析的支持向量机（PCA-SVM）准则改进区域生长的非结构化道路识别算法。首先，对非结构化道路颜色、纹理等复杂特征信息进行提取，采用PCA对提取的特征信息进行降维；然后，利用降维后的主元特征对SVM进行训练后作为复杂道路单元格的分类器。利用道路位置、起始单元格等先验知识以及道路边界单元格统计特征改进区域生长方法，在单元格生长时利用分类器判别，排除误判区域。实际道路检测结果表明，所提算法具有较好的鲁棒性，能够有效识别非结构化路面区域。对比结果表明，所提算法在保证准确率的同时，将10余维复杂特征信息压缩为3维主元特征，相比传统算法可缩短计算时间一半以上。针对背景与道路相似区域造成的传统算法10%左右的误判问题，所提算法能够有效排除。在野外环境下基于视觉的局部路径规划与导航方面，所提算法为缩短识别时间、排除背景干扰提供了可行途径。

关键词: 支持向量机, 主成分分析, 区域生长, 非结构化道路识别, 智能车辆