# Real-Time Traffic Sign Detection and Recognition Method Based on Simplified Gabor Wavelets and CNNs
## 5 Traffic Sign Classification  
The convolutional neural network (CNN) is a type of multi-layer neural network, which extracts features by combining convolution, pooling, and activation layers. The CNN is widely used in the field of pattern recognition. Many researchers have applied the CNN to traffic sign recognition and detection and have achieved good results. The CNN achieved perfect performances in the 2011 International Joint Conference on Neural Networks (IJCNN) [34; 35]. Recently, the CNN has been adopted in object recognition with high accuracy. Most of these models use raw images rather than hand-coded features, and most of them regard feature extraction and classification as a whole; this is known as end to end classification. Although raw image-based CNN image classification methods have achieved better performance, many scholars have also further researched the performance of CNN classification after feature transformation. The rotation invariant binary pattern-based feature, was used as the input for an artificial neural network in Reference [11]. Grey relational analysis was used before traffic sign recognition in Reference [36], and distance to border feature express was used as the input of the CNN in Reference [37]. Research into the expression of image features has attracted significant attention in the field of image classification. The Gabor filter attempts to imitate the characteristics of certain visual cells of mammals and has been successfully used as a preprocessor and feature extractor for a large variety of image processing applications, such as pattern recognition and analysis motion. In image classification fields, the Gabor feature has been widely used as the input of classifiers. In References [29; 37; 38], the Gabor feature was used for face recognition, mostly obtaining the performance of state-of-the-art methods. Many researchers have used the Gabor feature as an input of CNN and achieved better results. In the fingerprint image recognition area, Reference [39] used the Gabor filter to preprocess the raw image and made it the input of the CNN. In Reference [40], the speech signal was filtered by the Gabor filter, and then recognized by the CNN. In Reference [36], the Gabor filter was used as the first layer of the neural network and experiments regarding the open database in the area of face detection, digit recognition, and objection recognition, where it obtained a comparable performance with energy efficiency and fast learning during the training of the CNN. Therefore, the use of the special parameters of Gabor-based preprocessed images as the input of the CNN for image classification is very valuable research. In the detection stage of our method, we classified the traffic signs into super classes. We obtained information about the locations and sizes of the traffic signs in the traffic scene, and whether the traffic signs were circular or triangular signs. However, it is not known which subclass these traffic signs belonged to. In traffic sign design in China and Germany, there are no two traffic signs with the same shape and symbol but different colors; therefore, the traffic signs can be classified just by shape and the inside symbol. Moreover, it is also mentioned in Reference [41] that color information has little effect on the classification of traffic signs. As mentioned above, simplified Gabor filters can enhance the edge information of the image. This ability of the SGW can be used to strengthen the edges and internal symbols of traffic signs, which is conducive to shape-based classification. In our method, to classify the traffic signs, we used the simplified Gabor feature map as input, followed by the CNN. We designed two CNNs to classify the subclasses for the two super classes of traffic signs independently. One CNN was used to classify the subclass of circular traffic signs, and the other was used for the triangular traffic signs.