基于多尺度卷积神经网络的交通标志识别

针对自然场景中交通标志识别问题涉及的识别准确率和实时性改善需求,提出了一种改进的基于多尺度卷积神经网络(CNN)的交通标志识别算法.首先,通过图像增强方法比选实验,采用限制对比度自适应直方图均衡化方法作为图像预处理方法,以改善图像质量.然后,提出一种多尺度CNN模型,用于提取交通标志图像的全局特征和局部特征.进而,将组合后的多尺度特征送入全连接SoftMax分类器,实现交通标志识别.采用德国交通标志基准数据库(GTSRB)测试了所提算法的有效性,测试结果表明,算法在GTSRB基准数据集上获得98.82%的识别准确率以及每幅图像0.1ms的识别速度,本文算法具有一定的先进性.

Traffic Sign Recognition Based on Multi-scale Convolutional Neural Network

In view of the improvement requirements for accuracy and real-time performance of traffic sign recognition in natural scenes, an improved traffic sign recognition algorithm was proposed based on a multi-scale Convolutional Neural Network (CNN). At first, the comparison experiments on image enhancement methods was carried out, and contrast limited adaptive histogram equalization method was chosen as the preprocessing method to improve the image quality. Then, one kind of multi-scale CNN model was proposed to extract global and local features of the traffic sign images. Finally, the traffic signs were recognized after the combined multi-scale features were put into a fully connected SoftMax classifier. The effectiveness of the proposed algorithm was examined on the benchmark dataset---the German Traffic Sign Recognition Benchmark (GTSRB). The examination results show that the proposed algorithm can achieve 98.82% recognition accuracy and real-time processing with 0.1 ms per image on GTSRB dataset, which verified its superiority to some extent.