结合注意力机制的交通标志识别

Traffic sign recognition combined with attention network

智能交通体系中的无人驾驶这项课题是近年来一直是各大传统汽车行业甚至各大互联网巨头企业的研究热点。目前的无人驾驶技术以及辅助驾驶技术主要依赖于计算机视觉来采集道路交通标志信号,并通过分析系统实现对交通标志的处理及分类任务。现阶段的主要研究方法有传统方式的提取图片的HOG特征、SIFT特征等,之后送入SVM分类器或者贝叶斯分类器中。实现对于交通标志的提取与分类。近年来神经网络的迅速发展也为交通标志的识别贡献了新的力量,CNN,Faster R-CNN等网络的出现也被运用在交通标志的识别中。针对智能交通体系构建过程中的道路交通标志识别率较低的问题,本文将注意力机制引入到神经网络中,实现对交通标志图片的有效识别。该方法通过VGG网络实现对输入数据的特征提取,并加入递进的注意力网络实现对关注区域的放大以及细节提取,使得网络能够更有效地关注细节区域。将网络应用在比利时交通数据集上并取得了优秀的识别结果。最终的测试集分类准确率达到了98.2%。

The topic of unmanned driving in the intelligent transportation system has been a research hotspot in major traditional automobile industries and even major Internet giants in recent years.The current unmanned technology and assisted driving technology mainly rely on computer vision to collect road traffic sign signals,and through the analysis system to achieve the handling and classification tasks of traffic signs.At present,the main research methods are the HOG feature,SIFT feature,etc.of the extracted picture in the traditional way,and then sent to the SVM classifier or Bayesian classifier.Achieve the extraction and classification of traffic signs.In recent years,the rapid development of neural networks has also contributed to the identification of traffic signs.The emergence of networks such as CNN and Faster RCNN has also been used in the identification of traffic signs.Aiming at the problem that the recognition rate of road traffic signs is low during the construction of intelligent transportation system,this paper introduces the attention mechanism into the neural network to realize the effective identification of traffic sign pictures.The method realizes feature extraction of input data through the VGG network,and adds a progressive attention network to realize amplification and detail extraction of the attention area,so that the network can pay more attention to the detail area.The network was applied to the Belgian traffic dataset and achieved excellent recognition results.The final test set classification accuracy rate reached 98.2%.