车牌检测级联分类器快速训练算法

针对传统AdaBoost算法的不足,分析了训练过程中出现过训练及分类器退化的问题,并提出了解决这一问题的有效新方法.新方法主要对样本及时更新和样本权重的更新规则进行了调整.使用该方法训练级联车牌检测器,实验结果表明,新方法较好地解决了传统AdaBoost算法中所出现的过训练及退化问题,在保证检测率的同时降低了误检率,并且训练时间缩短了50%左右.

Improved license plate detection method based on AdaBoost algorithm

Focusing on the disadvantages of classical AdaBoost algorithm, this paper analyzes the issues of excessive training and overfitting for classifiers and proposes a new method to avoid these problems. The new method is to update the training samples in time and regulate the updated rules of sample weights. As a result, using the method to train a cascade license plate, the experimental results show that the new method does not lead to the issues of excessive training and overfitting like classical AdaBoost often does, and moreover, it will reduce false alarm rate with a high detection rate and the training time is shortened to 50%.