## 4 Experiments
### Ablation Study  
Comparisons of Different Architectures of MFFM  
In order to illustrate the proposed method, we have carried out many comparative experiments. These experiments are all done on the Caltech benchmark. These results were obtained from an average of five runs. Table 1 shows the comparisons of the main experimental methods. Under the condition of no shortcut structure and only adding 3 MFFUs to Res2 Net-DLA-60 directly, the performance has been obviously improved, from 4.75% to 4.31%, and the effect has also been improved to 4.16% when 6 MFFUs are added directly. This shows that our MFFU can directly improve the performance as a feature fusion unit when used only in series connection. If the shortcut structure without weights is directly added, the accuracy will be reduced whenever the levels are 3 or 6, compared with no shortcut structure. However, when the shortcut structure with weights is added, the effect has been greatly improved, which is far better than the no shortcut structure and the shortcut structure without weights. The results show that after adding MFFM of weighted shortcut structure with 3 MFFUs, compared with the case without MFFM, \(MR^{-2}\) has been significantly improved from 4.75% to 3.65%, and the number of model parameters is reduced from 42 M to 40 M, although the speed is reduced from 36.7\(FPS\) to 32.3\(FPS\). When the number of MFFUs of MFFM is 6, the speed is further reduced to 28.0\(FPS\), but the \(MR^{-2}\) is 3.71% without further improvement, probably because the features after 3 MFFUs are high enough for detection. The performance of 6 MFFUs and 3 MFFUs is basically the same under the \(IoU=0.5\) setting, but the accuracy of 6 MFFUs is lower under \(IoU=0.75\) setting. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{3}{*}{**Method**} & \multicolumn{3}{c}{**MFFM (level=)**} & \multicolumn{3}{c}{\(MR^{-2}\)**(\%)**} & \multirow{3}{*}{**Params (M)**} \\ \cline{2-3} \cline{5-7}  & \multicolumn{2}{c}{**shortcut**} & & & **IoU=** & & & **FPS** \\ \cline{2-7}  & \multicolumn{2}{c}{**weights/n**} & \multicolumn{2}{c}{**weights/w**} & **0.50** & **0.75** & & \\ \hline \multirow{6}{*}{R2-DLA} & - & - & - & 4.75 & 25.1 & 36.7 & 41.8 \\ \cline{2-7}  & 3 & - & - & 4.31 & 21.3 & 33.2 & 39.7 \\ \cline{2-7}  & - & 3 & - & 4.32 & 24.7 & 32.9 & 40.2 \\ \cline{2-7}  & - & - & 3 & **3.65** & **21.0** & 32.3 & 40.2 \\ \cline{2-7}  & 6 & - & - & 4.16 & 22.9 & 28.9 & 50.4 \\ \cline{2-7}  & - & 6 & - & 4.30 & 22.3 & 28.7 & 50.8 \\ \cline{2-7}  & - & - & 6 & 3.71 & 22.7 & 28.0 & 50.8 \\ \hline \multirow{2}{*}{SNv2} & - & - & - & 14.35 & 48.4 & **58.7** & 5.2 \\ \cline{2-7}  & - & - & 3 & 11.97 & 45.8 & 45.0 & **4.9** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparisons of Different Architectures of Multi-Level Feature Fusion Module (MFFM). In which, R2-DLA represents Res2 Net-DLA-60 and SNv2 represents ShuffleNetV2. And shortcut/n means no shortcut structure in the MFFM, and weights/n indicates that there are shortcut structures but without learnable weights in MFFM and weights/w indicates that there are shortcut structures with learnable weights. The number below MFFM (level=) represents how many Multi-Scale Feature Fusion Units (MFFUs) are added in the corresponding connection form of MFFM. After adding MFFM with level = 3, compared with the ShuffleNetV2 without MFFM, \(MR^{-2}\) improved significantly from 14.35% to 11.97%, the model parameters decreased from 5.2 M to 4.8 M, but the speed decreased from 58.\(TFPS\) to 45.0\(FPS\). No matter whether the backbone network is Res2 Net-DLA-60 or ShuffleNetV2, its performance has obviously improved after adding MFFM of \(level=3\). And both the parameters of the two networks are reduced after adding MFFM of \(level=3\), but the speeds are decreased. **Comparisons of Different External Structures of MFFUs**  
In terms of the connection modes of MFFUs in MFFM, we have tried many different structures.