为了提升道路交通标志的识别准确率以及实施性能, 本文提出一种改进的LeNet-5卷积神经网络结构对交通标志图像进行训练。首先在检测阶段, 采用基于颜色的轻量级分割算法和Hough变换 算法提取交通标志的目标区域, 并控制算法复杂度使该识别系统基本满足实时性要求, 再利用LeNet-5对交通标志进行分类识别。在实际的校园道路在线识别试验中进行检测, 结果表明, 18个交通 标志通过驾驶均在本文的算法中成功识别, 其运行速率达到16.9 Hz, 基本满足交通标志识别稳定、实时等性能要求。

In order to improve the recognition accuracy and implementation performance of road traffic signs, an improved LeNet-5 convolutional neural network structure is proposed to train 18 kinds of traffic sign images. Firstly, in the detection phase, the light-weight segmentation algorithm based on color and Hough transform algorithm is used to extract the target area of traffic signs, and the complexity of the control algorithm makes the recognition system basically meet the real-time requirements, and then LeNet-5 is used to classify and recognize traffic signs. The results show that the traffic signs are successfully identified by driving in the algorithm of this paper, and the running speed reaches 16.9 Hz, which basically meets the performance requirements of traffic sign recognition, such as stability, real-time and so on.