# Knowledge-Driven Network for Object Detection
## 3 Proposed Approach
### Backbone Parts  
**Input resolution. ** As mentioned before, the input of the network was \(512\times 512\times 3\) RGB images. Before the backbone network, we reduced the image resolution by 8 times using a 7 \(\times\) 7 convolution kernel module with a stride of 2 and a channel of 128, followed by a residual block with a stride of 2 and a channel of 256. Therefore, the resolution of the image after data preprocessing was reduced to 64 \(\times\) 64, which was in order to reduce the resolution of the original image to facilitate extraction features. **Backbone network. ** The backbone network can integrate the features of different levels of an input image and then carry out end-to-end classification. The layers of features can be enriched by training deeper models. Detectors based on the deep neural network typically use a classification network transmitted from the ImageNet classifications as a backbone. Thus, using a good backbone network has a great impact on the accuracy and efficiency of the model. Our method uses the same backbone network as SqueezeNet [49]. It was modified based on the standard SqueezeNet and adopted different convolution methods from the traditional ones; we adopted a 3 \(\times\) 3 depth-wise separable convolution to replace the 3 \(\times\) 3 standard convolution. The advantage of using this convolution is that it can reduce the parameter and calculations when the loss accuracy is not high. The entire feature extraction process was sampled using pooling, reducing the parameters while retaining the main object features. It then upsampled the features back to the original resolution by a series of upsampling layers and convolution layers. These novel modules can extract interesting features in the image for further classification and regression. **Corner pooling layers. ** Most of the algorithms based on the anchor-free framework use the method of corner pooling to aggregate the corners of the object. It is generally believed that the corners of the object tend to fall on the outside of the object and lack local appearance features. Corner pooling can solve this problem well. In the horizontal direction, there are changes from right to left to the maximum value of the currently traversed row elements. In the vertical direction, there are sequential changes from bottom to top to the maximum value of the column elements that have been traversed. After this, the  
Figure 2: An overview of knowledge-driven networks (KDNet) for object detection. In KDNet, we used a \(512\times 512\)-pixel image as input. Firstly, the input image was downsampled to 64 \(\times\) 64, and then the features were extracted through the backbone network to generate a feature map with the same dimension as the input. Secondly, the feature map generated a corner map through the independent corner pooling network and generated a heatmap using the corner map as its characteristic angle. Finally, the keypoint relation module (KRM) was used to model the relationship of each keypoint, and then, the keypoints with knowledge fusion were used to reposition the geometric position of the keypoints; thus, we obtained a more accurate heatmap. corresponding positions of the feature maps were added to obtain the final result. Taking the coordinates of the top left corner as an example, the top edge associated with the top left corner contains feature information at the top of the object; the left edge contains the feature information on the left side of the object. By gradually spreading the maximum value of the eigenvalues on both sides to the upper left corner area, and finally superposing, the response of the coordinates of this point will be larger than the sum of the other neighboring regions. The corner maps generated by this scheme can effectively aggregate the corner information of objects. Specifically, to determine whether a pixel is a top-left corner, we need to look horizontally towards the right for the topmost boundary of an object and look vertically towards the bottom for the leftmost boundary.