The classification of traffic sign images is easily affected by the change of weather, camera angles, occlusion, etc. The traditional image recognition methods not only require high image quality, but also need to find effective features manually. However, the convolutional neural networks can automatically extract high-level, abstract features which are robust to the variations. This paper presents a novel and effective traffic signs recognition approach via the kernel PCA network based on convolutional neural networks. The kernel PCA network uses two-layer convolutional network to extract ab...
