基于感兴趣区域的CNN-Squeeze交通标志图像识别  

Traffic Sign Image Recognition via CNN-Squeeze Based on Region of Interest

作　　者：张秀玲[1,2] 张逞逞 周凯旋 ZHANG Xiu-ling;ZHANG Cheng-cheng;ZHOU Kai-xuan(Key Laboratory of Industrial Computer Control Engineering of Hebei Province, Yanshan University,Qinhuangdao 066004, Hebei, China;National EngineeringResearch Center for Equipment and Technology of Cold Strip Rolling, Yanshan University,Qinhuangdao 066004, Hebei, China)

机构地区：[1]燕山大学河北省工业计算机控制工程重点实验室,河北秦皇岛066004 [2]燕山大学国家冷轧板带装备及工艺工程技术研究中心,河北秦皇岛066004

出　　处：《交通运输系统工程与信息》2019年第3期48-53,共6页Journal of Transportation Systems Engineering and Information Technology

基　　金：河北省自然科学基金(E2015203354);河北省教育厅科学研究计划河北省高等学校自然科学研究重点项目(ZD2016100);2016年燕山大学基础研究专项(理工类)培育课题(16LGY015)~~

摘　　要：在公路交通中,针对复杂环境下交通标志识别率不高的问题,提出了一种基于K-means对图像聚类,切割图像感兴趣区域(Regions of Interest, ROI),并利用方向梯度直方图特征(Histogram of Oriented Gradient, HOG)与卷积运算,特征加权(CNN-Squeeze)相结合的交通标志识别方法.首先,采用K-means对交通标志图像进行三角形、圆形图像二聚类,并利用制作的切割模板切割ROI并提取HOG特征;然后,利用卷积神经网络(Convolutional NeuralNetwork,CNN)对HOG特征进行过滤、降维,并通过Squeeze网络对过滤后的二次特征进行重要性标定;最后,训练该网络模型并实现对交通标志的识别.仿真结果表明,与BP网络、SVM及CNN对比,本文方法在保证训练时间的同时,识别精度达到98.58%.In highway traffic, in view of the low recognition rate of traffic signs in complex environments, a traffic sign recognition method based on K-means image clustering and image-cutting ROI is proposed, which combines HOG feature with convolution operation feature weighting (CNN-Squeeze). Firstly, K-means is used to perform clustering of triangles and circular images on the traffic sign image, and the ROI is extracted by using the cutting template. Then, the HOG feature is filtered and reduced by the CNN network, and the filtered secondary features are calibrated by the Squeeze network. Finally, the traffic sign is recognized by using the trained neural network. Simulation results show that compared with BP network, SVM and CNN network, this method can guarantee the training time and the recognition accuracy reaches 98.58%.

关 键 词：智能交通 K-MEANS 感兴趣区域 CNN-Squeeze 交通标志识别 

分 类 号：U268.6[机械工程—车辆工程]