# EAAnet: Efficient Attention and Aggregation Network for Crowd Person Detection
## 3 Experiment Results
### Ablation Study  
To further validate the effectiveness of EAAnet, dissection experiments were conducted. Table 4 presents the effects of adding the CBAM attention mechanism to different layers of the backbone. It is evident that compared to YOLOv5s, introducing CBAM at the third and fifth layers (the blocks in read in the backbone, as shown in Figure 7) resulted in the biggest increase of 1.4% in P but a 0.7% decrease in \(mAP50\). And only introducing CBAM to top layers in the backbone (such as the fifth and seventh, respectively) led to a limited increase of 0.2%, which resulted in the best \(mAP50\) of 70.0%. This indicates top layers have a close relationship to the positioning ability of the network. Meanwhile adding CBAM to the third, fifth, and seventh layer at the same time led to almost the worst performance in terms of both P and \(mAP50\) because the bottom layer provides attention to fine grain information (helps to find out the bounding box belongs to which category) of crowd, and after repeating convolution operations, fine grain information may get lost at the top layers and are more likely to output semantic information related to object position. Adding CBAM to each layer may result in redundancy, especially producing extra noise to both the precision and \(mAP50\). Thus, through the ablation experiments presented in Table 4, it is confirmed that while it may not yield optimal \(mAP50\) performance when adding CBAM modules simultaneously at both the third and fifth layers within our specific model application scenario, we prioritize the precise prediction of person number.  
And to further validate the effectiveness of adding a BiFPN on the neck, the ablation experiment results are presented in Table 5. It can be seen that YOLOv5s has a P of 76.8%, FLOPs of 15.8G, and 7.0M Params. Then, incorporating only CBAM led to a 1.4 improvement in P without changing the number of Params or FLOPs. It is worth noticing  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**3 Layer** & **5 Layer** & **7 Layer** & **P (\%)** & \(mAP50\) (\%)** \\ \hline \hline \multirow{4}{*}{\(\surd\)} & & & 76.8 & 69.8 \\  & & & 76.4 & 69.9 \\  & \(\surd\) & & 77.0 & 70.0 \\  & \(\surd\) & & 75.7 & 70.0 \\  & \(\surd\) & & 76.2 & 69.9 \\ \(\surd\) & & \(\surd\) & 77.3 & 70.0 \\ \(\surd\) & \(\surd\) & & 76.5 & 69.2 \\ \(\surd\) & \(\surd\) & & 78.2 & 69.1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: CBAM ablation experiments.  
Figure 8: Performance comparison. (**a**) P curve. (**b**) mAP 50.  
that by only adding the BiFPN module, there was a decrease of 3.3 in \(P\) along with growth in both Params and FLOPs by 0.1M and 0.2G FLOPs, respectively. This phenomena might be caused by using either improvement alone will lead to insufficient acquisition of fine-grained information related to detecting small objects at earlier stages in the backbone network. However, when applying CBAM and BiFPN together, it achieved the best performance, the biggest P of 78.6% which went up by 1.8% over YOLOv5s. Therefore, based on the ablation results shown in Table 4, it is evident that CBAM and BiFPN have mutually reinforcing effects and should be used together to effectively enhance model precision for target recognition.