# AspectNet: Aspect-Aware Anchor-Free Detector for Autonomous Driving
## 4 Experience and Results
### Validation Experiment  
First, the importance of the ignored area is discussed. Experiments verified that the ignored area not only reduced the loss value and increased its stability, but also improved the convergence speed significantly, as illustrated in Figure 9.  
The anchor-based methods set too many redundant anchor boxes, which resulted in the number of positive samples being much smaller than the number of negative samples: approximately 1:1000. The anchor-free methods discarded preset anchors and used key points for regression, but the imbalanced problem still existed. Table 1 indicates that the proportion of positive and negative samples affected the detection performance of the model. Unbalanced ratios led to a sharp decrease in the detection performance of the model. As shown in Table 2, by replacing the original sample definition methods of different models with the proposed method, the proposed sample definition method alleviated the imbalance problem, thereby improving model detection performance. Figure 9: Loss curves of the proposed method with or without the ignored area. The definition of the ignored region in the anchor-free method effectively accelerated the convergence degree of the model. The loss function in RetinaNet, CenterNet, and FCOS was replaced with the proposed loss function to test the effectiveness of the proposed loss function. Then the results were compared to those predicted by the original models. As shown in Table 3, the experimental results proved that the proposed loss function provided significant improvements, which indicates that the aspect ratio of the bounding box played a positive role in updating the model weights. As shown in Table 4, an aspect-aware head was added to different detection models. The experiments demonstrated that it helped the model to learn the aspect ratio distribution of different objects, thus improving the detection accuracy effectively. A comparison of the performance of the different approaches is summarized in Table 5, which includes the detection results for the anchor-based and anchor-free methods (CenterNet and FCOS). The anchor-based detectors included multi-stage algorithms and single-stage algorithms. AspectNet achieved high detection accuracy, thereby resulting in more competitive outcomes, with AP AP50 AP75 values of 76.2%, 97.3%, and 93.4%, respectively. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Sample Ratio** & **Backbone** & **AP** & **AP50** & **AP75** \\ \hline \multirow{4}{*}{Faster R-CNN} & 1:100 & & 52.1 & 81.8 & 65.2 \\  & 1:100 & & 63.5 & 90.8 & 77.5 \\  & 1:10 & & 68.9 & 94.2 & 84.4 \\  & 1:3 & & 70.2 & 94.7 & 86.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Detection results with different sample ratios on BCTSDB. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Backbone** & **Loss Function** & **AP** & **AP50** & **AP75** \\ \hline \multirow{2}{*}{RetinaNet} & \multirow{2}{*}{ResNet50} & Focal loss & 59.7 & 89.4 & 71.2 \\  & & Aspect aware loss & 61.8 & 91.2 & 72.6 \\ \hline \multirow{2}{*}{CenterNet} & \multirow{2}{*}{ResNet50} & Gaussian Focal loss & 69.4 & 95.6 & 85.4 \\  & & Aspect aware loss & 70.5 & 96.2 & 87.1 \\ \hline \multirow{2}{*}{FCOS} & \multirow{2}{*}{ResNet50} & Focal loss & 68.6 & 95.8 & 83.9 \\  & & Aspect aware loss & 70.8 & 97.2 & 84.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Detection results of different models with different loss functions on BCTSDB. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Sample Ratio** & **Backbone** & **AP** & **AP50** & **AP75** \\ \hline \multirow{4}{*}{Faster R-CNN} & 1:100 & & 52.1 & 81.8 & 65.2 \\  & 1:100 & & 63.5 & 90.8 & 77.5 \\  & 1:10 & & 68.9 & 94.2 & 84.4 \\  & 1:3 & & 70.2 & 94.7 & 86.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Detection results of different models with different sample definition methods on BCTSDB.