## 2 Vehicle Detection Framework Using Selective Multi-Stage Features
### Selective Multi-Stage Feature Extraction
Figure 3 shows each numbered layer, and the subsequent sections discuss the feature map visualization in this numerical order to describe the feature map characteristics in each layer. _(1) Verification Dataset_  
Figure 4 shows a typical example image from the validation dataset that was input into the CNN to visualize the feature maps in the trained CNN model in the subsequent sections. Figure 3: Proposed visualization technique for the CNN model. (2) Conv_1 Layer Visualization_  
There are 60 feature maps in the first convolution. The Conv_1 layer visualization result includes the resultant feature map extracted from the 5 \(\times\) 5 filter, where the white pixels show significant activation. The first convolution layer extracts the low-level features, such as edge information. Moreover, as it is the initial layer, there is little here that represents the vehicle image characteristics. (3) Relu_1 Layer Visualization_  
The first convolution layer feature maps are passed through the Relu function in the Relu_1 layer. As the Relu treats the feature map pixels with value < 0 as 0, the feature map is globally dark. This confirms that only low-level features, i.e., the edge information in the x direction, are learned. (4) Conv_2 Layer Visualization_  
There are 60 feature maps in the second convolution layer, and Figure 5a shows the resulting feature map extracted from the 5 \(\times\) 5 filter, where the white pixels show significant activation. The second convolution layer is also a lower layer, so mainly the low-level features are learned. There are two major differences between the first and the second convolution layer feature maps. 1. More edge information in the x direction is learned. The feature maps in the second convolution layer represent richer low-level features than those from the first convolution layer. Therefore, when we include low-level features in the fusion process, the second convolution layer feature maps are more appropriate than the first convolution layer ones. 2. Some features represent the vehicle head lamp, because the second convolution layer includes the low-level color information features. The head lamp area contains the characteristic vehicle information and can be differentiated from other objects. This particular characteristic information was not found in the other layers. Therefore, the feature map that contains the head lamp information must be included in the fusion process. Figure 4: Sample input image from the validation dataset. Figure 5: Example result of CNN’s layer visualizations: (**a**) Conv_2 Layer Visualization, (**b**) Maxpool_1 Layer Visualization, (**c**) Conv_5 Layer Visualization. (5) Relu_2 Layer Visualization_  
Relu_2 layer visualization result includes the Relu_2 layer feature maps, produced by passing the second convolution layer through the Relu function. The dark areas are similar to the Relu_1 layer outcome. (6) Maxpool_1 Layer Visualization_  
Figure 5b shows the Maxpool_1 layer feature map, which is the result of the max-pooling operation on the feature maps passed through the Relu_2 layer. Max pooling selects only the largest value in the subsampling window. Therefore, Maxpool_1 layer feature maps are stronger and more global than the second convolution or Relu_2 layer feature maps. The feature map visualization is similar to that from the second convolution layer because it is the result of only the max-pooling operation. (7) Conv_3 Layer Visualization_  
There are 60 feature maps in the third convolution layer. The Conv_3 layer visualization result includes the resulting feature map extracted from the 5 \(\times\) 5 filter, where the white pixels show significant activation. Edge information in the x direction is further enhanced, and edge information is concentrated in a specific area of the vehicle image. This is a middle-level feature, intermediate between the low- and the high-level features.