基于选择性搜索的交通标志检测方法

In order to solve problems that some current traffic sign detection algorithms are sensitive to light and deformation, difficult to set the threshold value, so they have low accuracy, less robustness or difficulty for meeting real-time requirements. A new traffic sign detection method is proposed based on selective searching, by full use of advantages of both segmentation-based method and the sliding window scheme detection method. It uses an improved hierarchical grouping algorithm to obtain object hypothesis regions of traffic signs, and then directly extracts HOG feature and trains these regions. It doesn't need exhaustive searching. The improved hierarchical grouping algorithm uses similarity strategy based on combined weights to merge the divided region for obtain more and better object hypothesis regions of traffic signs. Experimental results show that the proposed method achieves high accuracy ratio, robust to various adverse situations, and has a greater potential for real-time practical application.