# AspectNet: Aspect-Aware Anchor-Free Detector for Autonomous Driving
## 3 Proposed Method
### Aspect Prediction for AspectNet  
The unstable prediction of the aspect ratio of the bounding box can lead to wrong judgments by the autonomous driving decision-making module, as shown in Figure 4. Different objects have different aspect ratio distributions. For example, the expected value of the aspect ratio of the bounding box of the pedestrian is larger than that of the bounding box at the rear of the vehicle in front. The under-fitting of the model to the aspect ratio distribution of different objects makes model prediction unstable.  
An aspect-aware head is added at the end of the detector to predict the aspect ratio of objects that concatenates the classification and regression branches. This can solve the robustness problem mentioned above, as shown in Figure 3. The aspect-aware head improves the localization ability of the network. By contrast, learning the distribution of different aspect ratios can improve the model's classification ability.  
Specifically, by labeling the aspect ratio of objects, the model can learn the aspect ratio distribution of each specific category. Pedestrians have different weights, and the mean distribution is higher. Cyclists have a large distribution variance due to their different riding postures. Although vehicles have different viewing angles (e.g., aspect ratios of frontal and side views of vehicles are different), the overall aspect ratio distribution is lower than pedestrians and cyclists. By distinguishing the aspect ratio distribution of different categories, the model can locate the position of objects and classify them more accurately. During training, the aspect prediction head is trained synchronously with the other two  
Figure 3: The overall structure of AspectNet. The supervised model learns the categories, positions, and aspect ratios of objects. The feature map of the aspect-aware head consists of the classification and regression branches.  
branches and indirectly improves model performance by backpropagating the weight updates to these two branches so that the model can extract the aspect ratio features of the objects. During inference, the confident detection score of the predicted objects adds to the disturbance to the aspect ratio based on the existing predicted classification probability. The detection confident \(S_{i}\) score is defined as  
\[S_{i}=p_{i}^{1+|Asp_{i}-Asp_{i}^{reg}|}, \tag{1}\]  
where \(Asp_{i}\) is the predicted aspect ratio of the bounding box \(i\), \(Asp_{i}^{reg}\) is the aspect ratio calculated from predicted position information from the regression branch, \(p_{i}\) is the predicted classification probability. The larger the aspect ratio difference between the aspect-aware head and the regression branch, the smaller the confidence of the predicted bounding box.  
The aspect ratio \(Asp\) is defined as  
\[Asp=\frac{y_{2}-y_{1}}{x_{2}-x_{1}}, \tag{2}\]  
where \(x_{2}\), \(y_{2}\) are the coordinates of the lower right corner point of the bounding box, and \(x_{1}\), \(y_{1}\) are the coordinates of the upper left point. The feature maps are concatenated from the classification branch and regression branch. The entire aspect prediction head only consists of two convolution layers to keep the efficiency achieved by the RELU [22] activation layer.