# EAAnet: Efficient Attention and Aggregation Network for Crowd Person Detection
## 3 Experiment Results
### Comparison with SOTA Method  
For the COP dataset, EAAnet is compared with six mainstream object detection models, which include RetinaNet [30], Dynamic_RCNN [12], Atss [8], Faster R-CNN [7], YOLOv5, and YOLOv7, to prove its effectiveness. The experimental results are shown in Table 2.  
From Table 2, it can be observed that EAAnet has the highest \(p\) value of 78.6%, surpassing YOLOv5s by 1.8%. However, its performance on other indicators is slightly lower than YOLOv5s. For instance, EAAnet achieves mAP50 and mAP50:95 of 68.5% and 36.9%, respectively, which are reduced by 1.3 and 0.7 compared to YOLOv5s. Since the application scenario focuses on crowd evacuation in public places, the main goal is to precisely predict the number of people in order to ensure effective evacuation (as many people as possible) without knowing the exact location of each person. Therefore, this slight decrease does not significantly impact the final effectiveness of the model. The reasons for this decrease may be attributed to potential human biases in dataset labeling as well as a potential discrepancy between spatial attention and channel attention within the CBAM module when applied to the COP dataset. It is worth noticing that EAAnet adopts a lightweight backbone network structure, whereas YOLOv7 uses a deeper network structure, faster convolution operations, and smaller models. Theoretically, the parameter number of YOLOv7 should be smaller than the former, and the detection speed should be faster. However, in order to ensure the fairness of the experiment, we keep the network scale and configuration of EAAnet and YOLOv7 consistent. It is found that although the number of parameters of the two is close, the performance and computation of the latter are much lower than that of EAAnet. Moreover, Params and FLOPs of EAAnet reach 7.1M and 16.0G, respectively, which go up by 0.1 and 0.2. This is because of the introduction of BiFPN, which adds concatenation operations to cross-fuse feature maps. In summary, compared with other models, EAAnet exhibits the highest \(p\) value, indicating that its BiFPN structure effectively integrates features extracted from CBAM modules, thereby enhancing detection precision for the all category in the COP dataset. To further explore the precision of the algorithms in each category, we compared the performance of P on the two algorithms using the validation dataset in the COP dataset, \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **P (\%)** & **mAP50 (\%)** & **mAP50:95 (\%)** & **Params (M)** & **FLOPs (G)** \\ \hline YOLOv7 & 61.5 & 52.1 & 26.7 & 9.1 & 26.0 \\ Dynamic\_RCNN & 62.5 & 30.6 & 15.5 & 41.4 & 67.4 \\ RetinaNet & 68.0 & 28.0 & 13.5 & 36.4 & 57.1 \\ Atss & 71.0 & 40.6 & 20.5 & 32.1 & 55.9 \\ Faster\_RCNN & 74.5 & 47.3 & 23.5 & 41.1 & 67.4 \\ YOLOv5s & 76.8 & 69.8 & 37.6 & 7.0 & 15.8 \\ EAAnet & 78.6 & 68.5 & 36.9 & 7.1 & 16.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Data comparison of SOTA algorithms. and the result is shown in Table 3. The COP validation dataset has five categories, including all (25,937), pedestrians (17,038), riders (154), partially visible person (7602), and crowd (850). Obviously, EAAnet achieved optimal P in the all category, going up from 76.8% to 78.6% with an increment of 1.8. And it is worth noting that the riders and partially visible person categories have grown by 4.6 and 0.8, respectively, demonstrating that the improved algorithm has better performance for occluded or partially visible person. Although, for EAAnet, the pedestrians category has the highest P of 85.8% among all the categories, there is a decrease of 0.3 compared to YOLOv5s. This may be due to the large number of pedestrian instances bringing strong diversity and raising challenges for its detection. Moreover, the P of the crowd category is also decreased by 0.5, possibly because the extremely small object area leads to insufficient learning about this information.