# MS3D: A Multi-Scale Feature Fusion 3D Object Detection Method for Autonomous Driving Applications
## 4 Experiments and Analysis
### Experiment Analysis  
#### 4.4.1 Accuracy Analysis  
Tables 2-4 presents a comparative analysis of MS3D against the classical algorithms VoxelNet, SECOND, and PointPillars across easy, moderate, and hard difficulty levels on the KITTI dataset.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Frame Rate (HZ)**} & \multicolumn{3}{c}{**AP**} & \multicolumn{3}{c}{**F1 Score**} \\ \cline{3-8}  & & **BBOX** & **BEV** & **3D** & **BBOX** & **BEV** & **3D** \\ \hline VoxelNet & 4.4 & 0.69 & 0.82 & 0.81 & 0.47 & 0.49 & 0.50 \\ SECOND & 20 & 0.78 & 0.89 & 0.87 & 0.51 & 0.53 & 0.55 \\ PointPillars & 62 & 0.80 & 0.90 & 0.90 & 0.53 & 0.55 & 0.56 \\ MS3D & 60 & 0.84 & 0.94 & 0.93 & 0.54 & 0.56 & 0.58 \\ \hline \hline \end{tabular}
\end{table}
Table 2: A performance comparison of MS3D with other methods in the easy category.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Frame Rate (HZ)**} & \multicolumn{3}{c}{**AP**} & \multicolumn{3}{c}{**F1 Score**} \\ \cline{3-8}  & & **BBOX** & **BEV** & **3D** & **BBOX** & **BEV** & **3D** \\ \hline VoxelNet & 4.4 & 0.67 & 0.76 & 0.75 & 0.46 & 0.48 & 0.51 \\ SECOND & 20 & 0.76 & 0.85 & 0.84 & 0.50 & 0.53 & 0.54 \\ PointPillars & 62 & 0.78 & 0.864 & 0.863 & 0.53 & 0.55 & 0.55 \\ MS3D & 60 & 0.82 & 0.92 & 0.91 & 0.54 & 0.56 & 0.56 \\ \hline \hline \end{tabular}
\end{table}
Table 3: A performance comparison of MS3D with other methods in the moderate category.  
In Table 2, MS3D demonstrates superior performance in the easy category, achieving AP (average precision) scores of 0.84 for BBOX, 0.94 for BEV, and 0.93 for 3D, outperforming competing methods. Notably, MS3D's 3D AP surpasses VoxelNet by 12%, highlighting its strength in 3D object detection. In addition, MS3D's F1 Scores of 0.54, 0.56, and 0.58 in the BBOX, BEV, and 3D categories, respectively, also outperformed other methods, especially in the 3D category, with its F1 Score being 8% higher than Voxelnet, indicating a superior balance between precision and recall.  
In the moderate category of Table 3, the AP of MS3D reaches 0.82, 0.92, and 0.91 for the BBOX, BEV, and 3D object detection categories, respectively. This represents improvements of (0.15, 0.16, 0.16), (0.06, 0.07, 0.07), and (0.04, 0.06, 0.05) over VoxelNet, SECOND, and PointPillars, respectively. Additionally, the F1 Scores of MS3D are 0.54, 0.56, and 0.56 for the BBOX, BEV, and 3D categories, which are also better than other methods. Indeed, this indicates that MS3D has a significant advantage in terms of detection accuracy and generalization ability in the medium mode.  
In the difficult category, MS3D demonstrates superior accuracy over other methods. As indicated in Table 4, MS3D achieves higher AP scores across the BBOX, BEV, and 3D metrics. Specifically, for the 3D metric, MS3D attains an AP of 0.88, outperforming VoxelNet, SECOND, and PointPillars by 15%, 8%, and 5%, respectively. Furthermore, MS3D consistently delivers higher F1 Scores across the BBOX, BEV, and 3D metrics, underscoring its exceptional performance in complex detection tasks.  
Across three diverse categories, both PointPillars and MS3D excel in real-time performance, achieving 62 Hz and 60 Hz, respectively, demonstrating high-efficiency point cloud processing. MS3D, while maintaining rapid processing speeds, significantly enhances detection efficacy with its multi-scale feature fusion approach, offering superior accuracy and robustness, particularly in complex environments, despite a slightly lower frame rate than PointPillars.