Research on traffic sign recognition method based on multi-scale convolution neural network

基于多尺度卷积神经网络的道路交通标志识别方法研究

School of Automation, Northwestern Polytechnical University, Xi'an 710129, China

Received: 23 November 2020

Abstract

In order to accurately identify the traffic sign information under different road conditions, an improved deep learning method based on Faster RCNN model is proposed. Firstly, a multi-channel parallel full convolution neural network is designed to extract the color, shape and texture features of traffic signs in the original image. The multi-channel feature layers are fused to get the final feature map, and the adaptability of the model in various environment and weather conditions is enhanced by the image preprocessing. At the same time, the fusion features of deep and shallow feature layer are added into the feature extraction network, and the detailed texture information of shallow feature layer and semantic information of deep feature layer are retained, and the final feature layer can adapt to multi-scale change of traffic sign recognition. Secondly, the prior knowledge of traffic signs is used to detect and locate the target before the original RPN candidate region is generated. A more reasonable method for generating feature points and candidate anchor frames for traffic sign recognition is proposed. Based on the prior knowledge statistics of traffic sign size and proportion results, a target candidate frame suitable for traffic sign recognition is designed, a large number of redundant and negative correlation candidate frames is reduced, the detection accuracy and reduces the detection time is improved; secondly, the multi-scale candidate frame generation method for the deep and shallow feature layer is added to enhance the multi-scale target recognition ability and further strengthen the multi-scale target recognition ability Finally, this paper uses the international general traffic sign specification data set GTSRB/GTSDB and domestic traffic sign data set tt100k to verify the recognition ability of the model.

摘要

为了准确识别不同路况下的交通标志信息，提出一种在Faster RCNN模型基础上改进的深度学习方法。针对交通标志的显著特征，设计了多路并联全卷积神经网络，对原始图像中的交通标志颜色、形状以及纹理进行多路特征提取，将多路特征层进行融合得到最终特征图，通过图像预处理加强了模型在多种环境和天气状况下的适应能力。同时在特征提取网络中加入深浅层特征层的融合特征，保留浅层特征层的细节纹理信息和深层特征层的语义信息，得到最终特征层能够适应多尺度变化的交通标志的识别。在原有RPN候选区域生成网络前，利用交通标志先验知识作为辅助进行目标检测定位，提出了针对交通标志识别更加合理的候选锚框生成办法。从先验知识统计交通标志尺寸和比例结果出发，设计适用于交通标志识别的目标候选框，减少了大量冗余的和负相关的候选框，提高检测准确度减少检测时间；加入针对深浅特征层的多尺度候选框生成方法，在强化多尺度目标识别能力的同时，进一步加强了小目标检测和识别效果；采用国际通用交通标志规范数据集GTSRB/GTSDB以及国内交通标志数据集TT100K对模型识别能力进行识别验证。