# A Lightweight Model for 3D Point Cloud Object Detection
## 4 Experiment
### Quantitative Evaluation
Compared with LW-SECOND-3\({}^{\prime\prime}\) and LW-SECOND-3\({}^{\prime\prime\prime}\), the detection accuracy improves from 53.0% to 62.7%. It can be seen that there is a significant improvement in the effect, indicating \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Model** & **F(G)** & **P(M)** & **mAP** \\ \hline \multirow{4}{*}{KITTI} & SECOND & 69.8 & 5.34 & 63.3 \\  & LW-SECOND-1 & 52.4 & 3.62 & 55.3 \\  & LW-SECOND-2 & 20.2 & 0.93 & 51.2 \\  & LW-SECOND-3 & 18.5 & 0.67 & 48.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Experimental result for the lightweight model design. LW-SECOND-1 indicates the lightweight model designed by the first idea. LW-SECOND-2 indicates the lightweight model designed by the second idea. LW-SECOND-3 indicates the lightweight model designed by the third idea. F and P indicate the number of float operations (/G) and parameters (/M). mAP indicates the accuracy of the detection algorithm. that guidance and supervision with the loss of three parts is the best recovery effect on the accuracy of the student model. Regarding the experiments on nuScenes, we report the detection results in terms of the evaluation indicators mAP, NDS, mATE, mASE, mAOE, mAVE, and mAAE. The implementation details of the experiment are the same as those in Section 4.2. The experimental results are shown in Table 3. The table shows the evaluation of algorithm performance by different evaluation indicators. The larger the values of mAP and NDS, the better; the smaller the values of mATE, mASE, mAOE, mAVE, and mAAE, the better. Compared with LW-SECOND-3 and LW-SECOND-3\({}^{\prime\prime\prime}\), we can see that mAP and NDS increase by 10.3% and 9.4%, respectively. Figure 4 also provides the detailed prevision-recall (PR) curves for two datasets, showing the difference between the model after the lightweight model design with knowledge distillation and without knowledge distillation. It can be seen in the two ablation experiments that the loss function in this paper can effectively restore the accuracy of the lightweight model by using the three-part knowledge distillation losses to supervise and train the lightweight 3D point cloud object algorithm. It ensures that the lightweight model can meet the task requirements while reducing the complexity of the model. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Task**} & \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Car**} & \multicolumn{3}{c}{**Pedestrians**} & \multicolumn{3}{c}{**Cyclists**} & \multirow{2}{*}{**mAP**} \\ \cline{3-3} \cline{5-12}  & & **Easy** & **Mod** & **Hard** & **Easy** & **Mod** & **Hard** & **Easy** & **Mod** & **Hard** \\ \hline \multirow{4}{*}{BEV} & SECOND & 88.07 & 79.37 & 77.95 & 55.10 & 46.27 & 44.76 & 73.67 & 56.04 & 48.78 & 63.3 \\  & LW-S-3 & 69.81 & 61.72 & 57.86 & 44.28 & 38.53 & 33.61 & 54.49 & 41.19 & 36.64 & 48.7 \\  & LW-S-3\({}^{\prime\prime}\) & 70.62 & 61.57 & 58.61 & 45.09 & 37.34 & 31.43 & 55.27 & 40.93 & 37.46 & 48.8 \\  & LW-S-3\({}^{\prime\prime\prime}\) & 76.49 & 66.24 & 63.93 & 48.16 & 41.87 & 33.69 & 62.51 & 44.23 & 39.75 & 53.0 \\  & LW-S-3\({}^{\prime\prime\prime}\) & 87.37 & 79.12 & 77.81 & 53.94 & 45.65 & 43.56 & 73.39 & 56.11 & 47.63 & 62.7 \\ \hline \multirow{4}{*}{3D} & SECOND & 83.13 & 73.66 & 66.20 & 51.07 & 42.56 & 37.29 & 70.51 & 53.85 & 46.90 & 58.4 \\  & LW-S-3 & 68.45 & 60.97 & 57.51 & 41.38 & 33.87 & 29.06 & 58.82 & 44.16 & 36.21 & 47.0 \\ \cline{1-1}  & LW-S-3\({}^{\prime}\) & 69.54 & 61.20 & 56.07 & 40.26 & 33.04 & 30.54 & 56.32 & 41.51 & 37.62 & 47.2 \\ \cline{1-1}  & LW-S-3\({}^{\prime\prime}\) & 73.85 & 64.51 & 58.28 & 44.57 & 36.25 & 32.86 & 61.63 & 45.72 & 39.93 & 50.8 \\ \cline{1-1}  & LW-S-3\({}^{\prime\prime\prime}\) & 82.73 & 73.39 & 65.16 & 51.43 & 41.93 & 37.74 & 69.54 & 53.60 & 45.81 & 57.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental results for the KITTI dataset.