# A Real-Time Chinese Traffic Sign Detection Algorithm Based on Modified YOLOv2
## 3 The Chinese Traffic Sign Detection Algorithm Based on Modified YOLOv2
### Improved Network Structure for Chinese Traffic Sign Detection  
Convolutional networks are powerful models used in computer vision due to the feature hierarchies learned from the stacked convolutional layers. [PERSON] et al. (2016) indicated that the lower layers can learn some specific features of the object, such as edge, color, or texture information. The higher the layer, the more abstract are the features learned. To construct an effect network, we consider the networks for object classification as a reference. The development of the CNN structure occurred in several stages. LeNet ([PERSON], 2016) is a typical CNN used for handwritten digital recognition. LeNet only has five layers, which is very plain but quite effective. With the popularity of CNNs, deeper and deeper networks are proposed, such as AlexNet ([PERSON] et al., 2016), VGGNet ([PERSON], 2016), GoogLeNet ([PERSON], 2016), and ResNet ([PERSON], 2016). Each of these networks has its own strengths on image recognition and presents many efficient tricks or building blocks for constructing CNNs. From Table 1, we can see that the network structures of YOLO and YOLOv2 have several stacked convolutional layers, which are inspired by GoogLeNet and VGG models. The \(1\times 1\) convolutional layers followed by \(3\times 3\) convolutional layers are reduction layers that can increase the nonlinearity without changing the receptive fields of the convolutional layers. In Reference (VogogLeNet, 2016), the \(1\times 1\) convolutional layer is equivalent to the cross channel parametric pooling layer, which can \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **YOLO** & **YOLOv2** & **Model-A** & **Model-B** & **Model-C** \\ \hline Bottom & Conv7/2-64 & Conv3-32 & Conv3-32 & Conv3-32 & Conv3-32 \\ \hline Maxpool/2 & Maxpool/2 & Maxpool/2 & Maxpool/2 & Maxpool/2 & Maxpool/2 \\ Conv3-192 & Conv3-64 & Conv3-64 & Conv3-64 & Conv3-64 & Conv3-64 \\ Maxpool/2 & Maxpool/2 & Maxpool/2 & Conv1-32 & Conv1-32 \\ Conv1-128 & Conv3-128 & Conv3-128 & Maxpool/2 & Maxpool/2 \\ Conv3-256 & Conv1-64 & Conv1-64 & Conv3-64 & Conv3-64 \\ Conv1-256 & Conv3-128 & Conv3-128 & Conv1-32 & Conv1-32 \\ Conv3-512 & Maxpool/2 & Maxpool/2 & Conv1-32 & Conv1-32 \\ Maxpool/2 & Conv3-256 & Conv3-256 & Conv3-64 & Conv3-64 \\ Conv1-256 & Conv1-128 & Conv1-128 & Maxpool/2 & Maxpool/2 \\ Conv3-512 & Conv3-256 & Conv3-256 & Conv3-128 & Conv3-128 \\ Conv1-256 & Maxpool/2 & Maxpool/2 & Conv1-64 & Conv1-64 \\ Conv3-512 & Conv3-512 & Conv3-512 & Conv1-64 & Conv1-64 \\ Conv1-256 & Conv1-256 & Conv1-256 & Conv3-128 & Conv3-128 \\ Conv3-512 & Conv3-512 & Conv3-512 & Maxpool/2 & Maxpool/2 \\ Conv1-512 & Maxpool/2 & Maxpool/2 & Conv1-128 & Conv1-128 \\ Conv3-1024 & Conv3-1024 & Conv3-1024 & Conv3-256 & Conv3-256 \\ Maxpool/2 & Conv1-512 & Conv1-512 & Maxpool/2 & Maxpool/2 \\ Conv1-512 & Conv3-1024 & Conv3-1024 & Conv3-1024 & Conv3-512 & Conv3-512 \\ Conv3-1024 & Conv1-512 & Conv1-512 & Conv1-256 & Conv1-512 \\ Conv1-512 & Conv3-1024 & Conv3-1024 & Route & Route \\ Conv3-1024 & Conv3-1024 & Route & Conv3(256) & Conv3(128) \\ Conv3-1024 & Conv3-1024 & Conv3(512) & Conv1-64 & Conv1-64 \\ Conv3-2-1024 & Route & Conv1-64 & Reorg/Route & Route \\ Conv3-1024 & Conv1(64) & Reorg/Route & Conv3-512 & Conv3(256) \\ Conv3-1024 & Reorg/Route & Conv3-1024 & Conv1-40 & Conv1-64 \\ Local & Conv3-1024 & Conv1-40 & Detection & Reorg \\ Dropout & Conv1 & Detection & & Conv1(512) \\ Conn & Detection & & & Reorg/Route \\  & & & & Conv3-1024 \\  & & & & Conv1-40 \\ \hline Top & & & & Detection \\ \hline \hline \end{tabular}
\end{table} 
Table 1: ConvNet configurations. obtain the complex and learnable interaction information by crossing channels. It is important that the number of 1 \(\times\) 1 convolutional layers followed by 3 \(\times\) 3 convolutional layers is not a fixed value. To efficiently enhance the performance of detection framework for Chinese traffic sign, we considered the characteristics of traffic signs to help design the network structure.