# Real-Time Traffic Sign Detection and Recognition Method Based on Simplified Gabor Wavelets and CNNs
## 1 Introduction  
Traffic signs are road facilities that convey, guide, restrict, warn, or instruct information using words or symbols. With the development of automotive intelligent technology, famous car companies, such as Mercedes-Benz, BMW, etc., have actively invested in ADAS (Advanced Driver Assistance System) research. Commercialized ADAS systems not only include Lane Keep Assist Systems, but also include TSR (Traffic Sign Recognition) systems to remind drivers to pay attention to the speed. If drivers and pedestrians do not notice this information, it can lead to the occurrence of traffic accidents. With the increasing demand for the intelligence of vehicles, it is extremely necessary to detect and recognize traffic signs automatically through computer technology. Research in this area began in the 1980s, to solve this problem.  
To make them easy for drivers to read and recognize, traffic signs are often designed to be of a particular shape and color with symbols inside, so that there is a significant difference between the traffic signs and the background. For example, the speed limit 60 traffic sign is a circular shape with a strong number \"60\". These features are also important information for traffic sign recognition systems. However, traffic sign recognition is not an easy task, because there are many adverse factors, suchas bad weather, viewpoint variation, physical damage, etc. The difficulties in this area that we faced, were as follows:  
* Although the same kind of traffic signs have some consistency in color, in outdoor environments the color of the traffic signs is greatly influenced by illumination and light direction. Therefore, the color information is not fully reliable.
* As vehicle mounted cameras are not always perpendicular to the traffic signs, and the shape of traffic signs are often distorted in road scenes, the shape information of traffic signs is no longer fully reliable.
* Traffic signs in some road scenes are often obscured by buildings, trees, and other vehicles; therefore, we needed to recognize the traffic signs with incomplete information.
* Traffic sign discoloration, traffic sign damage, rain, snow, fog, and other problems, are also given as challenges in the process of traffic sign detection and classification.
* Some challenging examples are shown in Figure 1.  
In this paper, we present a real-time traffic sign recognition system consisting of traffic sign detection and classification. The main contributions of the paper are outlined as follows:  
* We propose simplified Gabor filters to preprocess the grayscale images of traffic scenes, to enhance the edges and strengthen the shape information. In addition, this could make the non-edge areas of painted artificial objects, such as traffic signs, more stable and reduce the noise in such areas.
* We use the maximally stable extremal regions (MSERs) algorithm to process the simplified Gabor filtered map to find the regions of interest more effectively, and we used our defined rules to filter out significant negative samples.
* We first used an eight-channel simplified Gabor feature as the input of the CNNs, which were defined as a pre-convolution layer of the convolutional neural networks (CNNs) for traffic sign classification.
* Our method performs only one feature extraction through the detection and classification stage, which causes feature sharing throughout the two stages. Compared with algorithms used in the different feature extraction methods, in the detection and classification stage, this saves a lot of processing time and makes it feasible for use in real time applications.  
The rest of our paper is organized as follows: In Section 2, we introduce the related work. In Section 3, we provide an overview of the method. The details of our traffic sign detection method are presented in Section 4. Section 5 describes the traffic sign classification method, and an experimental evaluation is provided in Section 6. Finally, we present some conclusions and future work.