# A Real-Time Chinese Traffic Sign Detection Algorithm Based on Modified YOLOv2
## 2 Related Work  
In retrospect, traffic sign detection methods can be roughly divided into two categories. One is based on traditional object detection algorithms and combines the characteristics of traffic signs. Another is characterized by deep learning-based approaches that have the ability to self-learn different features. Traditional detection methods mainly depend on the feature extraction algorithms. Regardless of traffic sign detection or traffic sign classification, they always require color features and shape features. With regard to color features, due to the limitation of algorithms based on RGB (Red, Green, Blue) color space for different light conditions on adaptability, the images are usually transformed to other color spaces, such as HSV (Hue, Saturation, Value) [8]. [PERSON] et al. presented a color probability model based on Ohta space in Reference [9] to compute probability maps for each color belonging to traffic signs. Considering the shape features, traffic signs are generally circular, triangular, rectangular, or polygonal. The edges are always used to extract contour lines by Hough transforms, radial symmetry, edge-following, etc. [PERSON] et al. proposed a new ellipse-detection method in Reference [10] to detect circular traffic signs deformed by external force or shooting angle. [PERSON] et al. [11] designed a set of templates for each object category of traffic signs to match shape. Besides the features of color and shape, there are some other feature descriptors used in traffic sign detection. [PERSON] et al. employed the histogram of oriented gradient (HOG) feature and SVM classifier [12] to detect traffic signs and obtained great grades in the German Traffic Sign Detection Benchmark competition hosted by the International Joint Conference on Neural Networks (IJCNN) in 2013. The boolean convolutional neural networks (BCNN) in Reference [13] combined the HOG features to detect traffic signs on GTSDB. The integral channel features (ICF) and aggregate channel features (ACF) were applied to USA traffic sign detection separately [14]. The features extraction algorithms are more efficient on linear features than non-linear features. Although hand-crafted features have achieved a higher precision for traffic  
Figure 3: Samples of the three super categories in China. signs, it is worth noting that the traditional detection methods have stronger pertinence but lack the robustness for the whole system. Recently, deep learning has been popular in use among computer vision research. There are two approaches to object detection related to deep learning. One is based on region proposal and is also called a two-stage method. As the groundbreaking method in terms of industrial applications solutions, region-based convolutional neural networks (R-CNN) [15] notably improves the average detection precisions on visual object classes challenge (VOC). R-CNN generates 2 k regions of interest (ROI) by selective search and extracts features by CNNs separately. At the last stage, R-CNN employs the SVM classifier to predict the classes of objects. To obtain a better performance, it also adopts linear regression to fine-tune the positions and sizes of detection boxes. Since the spectacular effect of R-CNN, there have been many new ideas implemented on CNN, such as spatial pyramid pooling network (SPP-Net) [16], Fast R-CNN [17], and Faster R-CNN [18]. The precision and speed on object recognition of the above methods improved rapidly and the fastest fame rate could reach 15 fps. The other approach is based on the regression method, which is an end-to-end learning model without classifiers and is also called a one-stage method. [PERSON] et al. successively presented the YOLO algorithm [4] and YOLOv2 [7], which integrate object detection and object classification into a single convolutional network, enhancing the speed remarkably but losing the precision. Subsequently, the single shot multibox detector (SSD) [19] introduces the default boxes and multi-scale feature mapping layers to raise the precision.