# Stereo 3D Object Detection Using a Feature Attention Module
## 4 Experiment
### PatchNet Local Fusion and Model Interpretability  
This study used the depth estimation results obtained by the PSMNet model [12] provided by [PERSON] et al. [4] as the source of stereo point cloud data and trained 100 epochs using the frame of the PatchNet model. The model used 0.001 as the initial learning rate, which decayed at a rate of 0.1 at the 40 th and 80 th epochs. Because the late fusion method has the most significant fusion effect, the feature attention module was inserted during the late fusion process to observe the feature weights. The feature weights of the two parts expressed in numerical form were obtained. The feature fusion method was explained based on the weight and visualization results. Referring to the official evaluation method of the KITTI dataset, the object to be detected was divided into three difficulty levels (Easy, Moderate, and Difficult) based on the size of the object to be detected in the image, degree of occlusion, etc. The Table 1 shows the detection accuracy of bird's-eye view (BEV) perspective/3D view of the original PatchNet model and PatchNet with late fusion. With the comparison of detection based only on the point cloud and with late fusion strategy, we can see that the detection of small objects (Pedestrian and Cyclist) has clearly improved. After that, we calculated the weight of different features by using our feature attention module; the results can be seen in Table 2.  
We can see that the image feature weight of small objects was evidently higher than that of cars. We then visualized the results. We made a figure to depict the feature weight distribution (Figure 8). The red color represents the area where the point cloud has a higher weight than the image, and the blue color represents the area where the image weight is higher than that of the point cloud. We can see the two kinds of feature weight distributions. The important image feature area is represented by the blue color, which correlates with the position and contour of the objects in the image block. For small objects, due to their small area in the image, the area and weight of the image features will be relatively large. We analyzed the reason behind this phenomenon. Specifically, since the objects must be detected and located by the point cloud feature, the main part of the objects always has a higher point cloud weight. Given that the image can help us better locate the object's position in the image and distinguish the foregro \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Category**} & \multicolumn{3}{c}{**PatchNet**} & \multicolumn{3}{c}{**PatchNet with Late Fusion**} \\ \cline{2-6}  & **Easy** & **Moderate** & **Difficult** & **Easy** & **Moderate** & **Difficult** \\ \hline \multirow{2}{*}{Car} & **76.90/** & 53.00/ & **44.09/** & 76.39/ & **53.21**/ & 42.92/ \\  & **68.44** & **41.84** & **33.90** & 65.17 & 41.35 & 33.41 \\ \multirow{2}{*}{Pedestrian} & 40.11/ & 31.73/ & 26.30/ & **41.42/** & **32.90/** & **26.75/** \\  & 33.53 & 26.13 & 21.35 & **36.19** & **28.29** & **23.14** \\ \multirow{2}{*}{Cyclist} & 40.38/ & 22.13/ & 20.43/ & **42.64/** & **23.93/** & **22.27** \\  & 36.56 & 20.02 & 18.36 & **36.83** & **20.50** & **18.97** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Detection results based on PatchNet. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Category/Feature** & **Point Cloud** & **Image** \\ \hline Car & 0.77 & 0.23 \\ Pedestrian & 0.73 & 0.27 \\ Cyclist & 0.68 & 0.31 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The weight of the feature attention module. the image feature occupies the background area, near the outline of the objects. And, as discussed in Section 1 of this article, the gain effect of the image segmentation method on 3D object detection can be discussed.