# Object Detection in Autonomous Driving Scenarios Based on an Improved Faster-RCNN
## 3 Proposed Algorithm: AD-Faster-RCNN
### Backbone Network Design
#### 3.2.1 Deformable Convolution Suitable for Autonomous Driving Scenarios  
In the autonomous driving scene, where the object scale is different and the shape is changeable, the detection of objects using traditional convolutional neural networks is not efficient enough because of the inherent geometric structure (regular sampling point) of the convolutional neural network. The deformable convolution used in this paper modifies the ordinary convolution and improves the deformation modeling ability of the CNN. The basic idea is to learn an offset for the sampling point so that the convolution kernel focuses on the region of interest or object instead of sampling at a fixed location. The comparison between normal convolution sampling and deformable convolution sampling is shown in Figure 2.  
Figure 1: The AD-Faster-RCNN model structure.  
The calculation method of the traditional convolution structure is as follows:  
\[y(p_{0})=\sum_{p_{n}\in R}w(p_{n})x(p_{0}+p_{n}) \tag{2}\]  
In the formula, \(w\) represents the weight of each sampled value, \(p_{n}\) is the output position of the convolution, and \(p_{0}\) corresponds to an integer offset to provide nearby semantic information.  
Deformable convolution adds a small offset \(\Delta p_{n}\) to traditional convolution, which is calculated by another convolution and can be taken to a decimal offset. The updated convolution calculation method is as follows:  
\[y(p_{0})=\sum_{p_{n}\in R}w(p_{n})x(p_{0}+p_{n}+\Delta p_{n}) \tag{3}\]  
In the formula, \(\Delta p_{n}\) represents the offset of each sampling point.  
After increasing the decimal offset, the corresponding position cannot be found directly in the image of the upper layer during the convolution calculation. The application needs to obtain the corresponding value through methods such as bilinear interpolation.  
Since deformable convolution breaks the shape of the conventional sampling area, the sampling points may be expanded to the part outside the area of interest during the use of the model, and more irrelevant information and context information may be included, which would affect the performance of the model. Therefore, deformable convolution v2 [24] proposes an improved scheme. The parallel network not only learns the offset value of each position but also learns the weight of each sampling point to avoid the influence of extreme sampling points on network feature extraction. Through weight control, the influence of having too much context information can be effectively reduced, a greater degree of freedom can be added, and the weights of sampling points that may not be needed can be learned to bring them to zero. The calculation formula then becomes:  
\[y(p_{0})=\sum_{p_{n}\in R}w(p_{n})x(p_{0}+p_{  
and difficulty in training the network. To reduce the waste of computing resources, deformable convolution is applied only in the last two stages of the backbone network in the experimental process of this paper. A good balance is thus achieved between parameter quantity and feature extraction.