关键词: 卷积神经网络, 交通标志识别, 尺度依赖池化, 特征融合, 空间金字塔池化

Abstract: Aiming at these problems that the traffic sign has a small proportion in the natural scene, the extracted features are insufficient and the recognition accuracy is low, an improved Scale Dependent Pooling (SDP) model was proposed for the recognition of small-scale traffic images. Firstly, because the deep convolution layer of neural network has better contour information and class characteristics, Supplementary Deep convolution layer characteristic Scale-Dependent Pooling (SD-SDP) model for deep convolution layer characteristic was used to extract features based on the feature information of shallow convolution by SDP model, enriching feature information. Secondly, the Multi-scale Sliding window Pooling (MSP) was used to make up the edge information of the target object, instead of the single-layer spatial pyramid method in the original SDP algorithm. Finally, the improved SDP model was applied to the recognition of traffic signs. The experimental result show that, compared to SDP algorithms, the extracted feature dimension increases and the accuracy of small scale traffic image recognition is improved.

Key words: Convolution Neural Network (CNN), traffic sign recognition, scale dependent pooling, feature fusion, spatial pyramid pooling