基于感兴趣区域的CNN-Squeeze交通标志图像识别

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 Kmeans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 Neural Network, 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%.

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

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%.