# EAAnet: Efficient Attention and Aggregation Network for Crowd Person Detection
## 4 Discussion  
In order to visually explore the performance of EAAnet from different perspectives, combined with the validation set of the COP dataset, comparative analyses of the detection effects and heat maps are conducted horizontally. Vertically, the performance of the model on different categories and confusion matrices is plotted. Firstly, typical images from the validation set are selected for visual display. As shown in Figure 9, each row represents normal scenes (few individuals and minimal occlusion), dense scenes (concentrated crowd with a horizontal arrangement and severe occlusion), and dense complex scenes (complex background, diverse vehicle types, and extremely severe occlusion). In each row, the first column shows the original images from the COP dataset, the second column shows the predictions by YOLOv5s and the third column shows the predictions by EAAnet. Specifically, as shown in the first-row image, at the bottom right of the image, a pedestrian obscured by a street lamp is predicted by EAAnet, while it is not labeled in the original dataset annotations or predicted by YOLOv5s. Furthermore, as seen in images in the second row, both EAAnet and YOLOv5s find a woman wearing a black blouse which is not annotated in the original dataset. Additionally, through comparison in the third row, it can be observed that EAAnet predicts people in the left middle and bottom middle part of the image, which are not labeled or predicted. Thus, it proved that EAAnet has strong detection capability, adaptability, and robustness, especially near the image boundaries, so as to more accurately predict numbers of crowd persons for various scenarios. Heat maps are usually used to better observe whether the added modules extract feature information correctly. In the heat map, areas with brighter colors indicate more contribution to the prediction. As shown in Figure 10, three groups of feature heat maps are selected at the 0 th, 5 th, and 19 th layers of the EAAnet structure layer, which represent the result of preprocessing, adding CBAM, and adding both CBAM and BiFPN. It is found that the extracted features change from simple textures to more complex and comprehensive semantic information as the layers go deeper. Moreover, the roughly corresponding areas of people are almost all in bright yellow, proving that EAAnet can effectively extract and integrate fine-grained information about small objects, thereby significantly improving both the precision and efficiency of detection models. To further explore the performance differences in EAAnet for each category for the COP dataset, P curves, R curves, PR curves, and F1 curves were plotted separately as shown in Figure 11a-d. In Figure 11a, the relationship between the different categories and P values is displayed. Specifically, all the categories tend to overlap each other when the confidence level tends to be one. Figure 11b describes the variation in recall rates at different confidence levels; lower recall values indicate better performance. It can be seen that there \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**CBAM** & **BiFPN** & **P (\%)** & **Params (M)** & **FLOPs (G)** \\ \hline  & & 76.8 & 7.0 & 15.8 \\ \(\surd\) & & 78.2 & 7.0 & 15.8 \\  & \(\surd\) & 73.5 & 7.1 & 16.0 \\ \(\surd\) & \(\surd\) & 78.6 & 7.1 & 16.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: BiFPN ablation experiments. is a relatively dispersed curve for each category and a particularly good performance for the crowd category. Figure 10(c) represents the P-R curve; normally, higher precision corresponds to lower recall. An ideal curve is a curve that is convex towards the upper right corner with a large enclosed area representing an AP value. Therefore, it can be concluded that the pedestrians and riders category are better. In Figure 10(d), F1 serves as a harmonic parameter between P and R, where the higher the better. It can be observed similarly that the model exhibits better performance for the pedestrians category.