# FDCNet: Frontend-Backend Fusion Dilated Network Through Channel-Attention Mechanism
## 4 Experiments
### Performance on Common Datasets
#### 4.4.5 Ablation Study  
We perform the ablation study to validate the efficacy of our FDCNet. Several ablation experiments are conducted on ShanghaiTech Part A dataset. The comparisons are shown in Figure 4, where the proposed framework gets the best estimation. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **MAE** & **MSE** \\ \hline MCNN [16] & 1.07 & 1.35 \\ SwitchCNN [8] & 1.62 & 2.10 \\ CSRNet [11] & 1.16 & 1.47 \\ FDCNet & 1.25 & 1.45 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The estimation errors on UCSD dataset. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **MAE** & **MSE** \\ \hline MCNN [16] & 377.6 & 509.1 \\ SwitchCNN [8] & 318.1 & 439.2 \\ SaCNN [12] & 314.9 & 424.8 \\ CSRNet [11] & 266.1 & 397.5 \\ FDCNet & 246.8 & 322.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The estimation errors on UCF_CC_50 dataset. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **MAE** & **MSE** \\ \hline R-FCN [32] & 6.02 & 5.46 \\ Exemplary Density [33] & 1.82 & 2.74 \\ FDCNet & 1.80 & 2.52 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The estimation errors on Mall dataset. * VGG-16 framework  
To verify the effectiveness of our method, we first did experiments on the backbone VGG-16 framework. The result in Figure 4 shows that our proposed framework improved the estimation more than the VGG-16, which implies that the improvements of our network are efficient and feasible. Figure 3 demonstrates the output density maps from VGG-16 framework. Compared with the backbone results, we can see that our framework remedied the background interference and the perspective problem because the density maps are accurate in locating the head and eliminating the background. Furthermore, it is more suitable for congested images than VGG-16, as seen in the three extremely dense images. * One-fusion framework  
We have another choice to fuse different levels of feature maps before the dilated block, this is shown in Figure 5. The convolutional layers are set as FDCNet. There is only one fusion that Conv1_2, Conv2_2, Conv3_3, Conv4_3, Conv5_3, and Conv6_3 are concatenated together in the framework. After fusion the feature map is fed into the channel-attention block. We trained using the ShanghaiTech Part A dataset on this one-fusion framework and compared the estimation with FDCNet. Seeing the results in Figure 4, the errors of one-fusion are much worse than FDCNet. It is obvious that FDCNet shows a more brilliant performance than the one-fusion framework. On the one hand, the fusion of two levels can capture more varied scale features and send them to the next convolution, so that the backbone can extract more information. On the other hand, more channel-attention blocks following the fusion adjust them and obtain more spatial information. Owing to these factors, the FDCNet performs much better. Figure 4: The estimation errors of the ablation study. Figure 5: The architecture of the one-fusion framework. * The channel-attention block  
We removed the three channel-attention blocks and let the output of the concatenation feed directly into the next convolutional layers. We can see from Figure 4 that the accuracy without channel-attention blocks are lower, which proves that the channel-attention block brings an awesome improvement with a slight computation. * The dilated block  
We do the experiments on our model to inspect the effect of using the dilated convolution. In the ablation study, the dilated layers in the dilated block are all replaced with the normal convolutional layers. Figure 4 shows that both the MAE and MSE are much worse than that of FDCNet, respectively reduced by 6.5% and 3.7%. The results reflect that dilated convolution works better than the normal convolution without increasing the number of parameters or the amount of computation. * The loss function  
We also do experiments using only the L\({}_{2}\) loss function. Figure 4 reflects that both of the MAE and MSE of the fusion loss function are better.