# An Improved Differentiable Binarization Network for Natural Scene Street Sign Text Detection
## 3 Proposed Method
### Improved DBNet for Natural Scene Text Detection
#### 3.2.1 High-Level Feature Enhancement Model  
For text detection networks, semantic contextual information plays a crucial role in scene text detection. To be able to detect both smaller and larger text in the same image and obtain good detection results, rich contextual information must be extracted from the image.  
Resnet-18 first extracts feature maps {C2, C3, C4, C5} from the input images. C5, as the highest semantic feature map of the FPN, has many channels of information. However, when the FPN performs feature fusion from the top down, the number of channels of the top-level feature map must be reduced to the same number of channels of the bottom-level feature map to which it is fused to be able to fuse with the rest of the feature maps. Therefore, the number of channels is reduced in the process of downward propagation of the top-level feature map, resulting in the loss of semantic information in the network. To solve this problem, we proposed a feature enhancement module to enhance the high-level semantic information in this study, which reduced the loss of feature information and improved the detection effect of the net model.  
As shown in Figure 6, first, the high-level semantic feature map \(C5\) was used as the input, and four feature maps of different scales were obtained after applying the following adaptive average pooling operation:  
\[f_{k}=\alpha_{k}C5:k=1,2,3,4 \tag{3}\]  
where \(k=1\), \(2\), \(3\), \(4\) denotes the four generated feature maps and \(\alpha_{k}\) denotes the \(\alpha_{k}\)-fold downsampling operation on \(f\)5. In the experiments, \(\alpha_{1}\), \(\alpha_{2}\), \(\alpha_{3}\), and \(\alpha_{4}\) were set to \(1\), \(0.5\), \(0.4\), and \(0.2\), respectively.  
Figure 5: Network structure diagram of improved DBNet model.  
After downsampling, the channels of these four feature maps were changed to 256 dimensions through \(1\times 1\) convolutions, and the four feature maps were, respectively, normalized to a uniform scale by \(\frac{1}{\alpha_{k}}\) upsampling through 3 \(\times\) 3 depthwise separable convolution and linear interpolation upsampling methods. Then, the four feature maps were fused into a feature map of the same size as f5 through adaptive feature fusion (AFF), and finally, feature fusion was performed with f5.  
The feature-enhanced network obtained different feature maps through adaptive pooling with a constant scale, and it obtained perceptual fields of different sizes after the 3 \(\times\) 3 convolution layers, which increased the amount of information of the image context obtained by the top-level feature map. This facilitated text detection at different scales. In order to increase the convergence speed of the network, the parameters of the obtained feature maps were normalized by using BN layers, and they were finally passed to the rectified linear unite (ReLU) activation function and fused with f5 for features. In this way, the more contextual information of different spaces was incorporated into the feature map f5, so that the bottom-level feature map could obtain more semantic information propagated by the top-level feature map, thereby improving the text detection ability of the model.