# Real-Time Traffic Sign Detection and Recognition Method Based on Simplified Gabor Wavelets and CNNs
## 2 Related Works  
In general, traffic sign recognition mainly includes two stages: the first stage is traffic sign detection, which concerns the location and size of the traffic signs in the traffic scene images, and the second stage of the process is traffic sign recognition, which pays close attention to the classification of what exact class the traffic signs belong to. Traffic sign detection is usually based on the shape and color attributes of traffic signs, and traffic sign recognition is often used with classifiers, such as convolutional neural networks (CNNs) and SVM with discriminative features. Figure 1: Examples of the difficult situations faced in traffic sign detection and recognition: Undesirable light, disorientation, motion blur, color fade, occlusion, rain, and snow. Traffic signs have a strict color scheme, which includes red, blue, and white that allow us to distinguish traffic signs from the background scene. It is not difficult for human beings to distinguish traffic signs from a background, so for a computer detection system, color information is also an important feature. In the present research, color-based traffic sign detection method is shown to be the most straightforward and simplest method [1; 2; 3; 4]. As red, green, blue image (RGB) is too sensitive to illumination, color space conversion algorithms have often been applied to traffic sign detection. For example, [PERSON] et al. [5] segmented the image by the U and V chromaticity channels in YUV space, where U is positive and V is the color red. This information was combined with the hue channel of the hue, saturation, value (HSV) color space to segment the red road signs from the image. In Reference [6], thresholds were used in the color channel of the HSV color space, to segment the red road signs. Unfortunately, due to strong light, poor light, and other bad weather conditions, color-based detection methods often fail to achieve better results. Except for countries like Japan and a few others, traffic signs have pairs of different signs when converted to grayscale which appear the same, and traffic signs from most countries have a strict shape and symbol distinction. This design allows color blind and color weak drivers and pedestrians to recognize traffic signs without or with less color information. Based on such features, some algorithms completely ignore color information and adopt shape-based image segmentation [7; 8; 9]. In addition to the above-mentioned factors, some algorithms use image features other than the raw color space and shape information. The authors in Reference [10] used the color probability model, and traffic sign proposals were then extracted by finding the most stable extremal regions on these maps. Reference [11] used rotation invariant binary pattern-based features. In any case, finding good feature expression is an important part of traffic sign detection, and is also an important research topic in this field. Recently, as deep learning methods have demonstrated prominent representation capacity and achieved outstanding performance in traffic sign recognition, more scholars have applied technologies to this area [12; 13; 14; 15]. [PERSON] [12] used a convolution neural network based on deep learning with a hinge loss stochastic gradient descent method, which achieved a high recognition rate. [PERSON] [13] provided a multi-column deep neural network for traffic classification running on a graphic processing unit (GPU), and obtained a better-than-human recognition rate. [PERSON] [14] used CNN as the classifier to learn the discriminative feature of max pooling positions to classify traffic signs and obtained a comparable performance with the-state-of-the-art method. [PERSON] [15] used a procedure based on color segmentation, histogram of oriented gradients, and CNN, for the detection and recognition of traffic signs, which achieved better classification accuracy and computational speed.