# A Real-Time Chinese Traffic Sign Detection Algorithm Based on Modified YOLOv2
## 1 Introduction  
Traffic Sign Recognition Systems (TSRs) are important for highway maintenance, driver-assistance systems, and self-driving cars [1]. In general, the research of traffic sign recognition is divided into sign detection and sign classification, which both have been discussed for a long time; in particular, the latter has achieved greater success on the German Traffic Sign Recognition Benchmark (GTSRB). As one of key technologies of TSR, traffic sign detection faces some difficulties in real traffic scenes due to illumination changes, partial occlusion, cluttered background, and small size, as shown in Figure 1.  
In the past, the mainstream methods for traffic sign detection were based on traditional object detection algorithms. The pipeline of traffic sign detection generally uses hand-crafted features to extract region proposals, and then combines classifiers to filter out the negatives. Nowadays, deep convolutional networks are applied to image recognition and object detection, becoming increasingly fast and accurate. Convolutional neural networks (CNNs) can learn features from a large number of samples without preprocessing, which avoids the design difficulty of hand-crafted features and learns more generalized features. CNN has already been presented as a classifier in machine learning and has been used in traffic sign classification. The performance of the CNN-based method is the state-of-the-art on GTSRB [2]. However, for traffic sign detection, the CNN-based method [3] is not superior to real-time performance due to the complex computation. However, the graphics processing unit (GPU) can solve this problem through its high computing performance. Up to now, many newtypes of convolutional networks for object detection have emerged and been proven to be efficient in relevant fields. The series of methods based on regression exhibits real-time performance; for example, the speed of YOLO [4] can reach above 45 frames per second. To evaluate the relative methods for traffic sign detection, there are several public traffic sign datasets available, such as GTSDB [5] and LISA traffic sign dataset (LISASTSD) [1]. GTSDB is most commonly used by researchers. It is noteworthy that the traffic signs are classified coarsely into mandatory signs, danger signs, and prohibitory signs in GTSDB. The three categories of traffic signs are common and simple, featuring universal design principles compared to other signs. For researching Chinese traffic sign detection methods, there is a recently published Chinese traffic sign dataset (CTSD) [6]. Figures 2 and 3 show some samples of the three super categories in Germany and China, respectively. There are some similarities and differences among the three super categories. Some of Chinese traffic signs contain Chinese characters, not just English characters. We found that there are only slight differences in some traffic signs, but others have completely distinct representation forms, such as the sign prohibiting overtaking. The details should be taken into account when detection methods are applied to Chinese traffic signs. In this paper, to achieve real-time traffic sign detection on Chinese roads, we expanded the CTSD by adding new images and transforming images. The expanded CTSD is also called Changsha University of Science and Technology Chinese traffic sign detection benchmark (CCTSDB). More importantly, we propose an end-to-end convolutional network for Chinese traffic sign detection inspired by YOLOv2 [7], which is a unified detection model without a complex processing pipeline. Figure 1: Problems of traffic sign detection. Figure 2: Samples of the three super categories in Germany. Furthermore, we made full use of the convolutional layers and characteristics of traffic signs to construct three kinds of networks. Finally, we selected the Model-B through experiments performed on CCTSDB. This method proved to be faster and more robust than other network structures. In addition, our method was also evaluated on GTSDB and the results compared well with other methods.