# EAAnet: Efficient Attention and Aggregation Network for Crowd Person Detection
## 4 Discussion
In summary, from these above perspectives, it has been confirmed that EAAnet demonstrates superior performance in crowd person detection tasks. In addition, compared with the ROC curve (drawing the relationship between true match rate and true match rate to compare the performance of different binary classification models, and by observing the curve, an appropriate threshold can be selected to balance the sensitivity and specificity of the model), the confusion matrix provides an intuitive basis about all the categories. And except for the main diagonal representing the true positive rate, the sum of the horizontal axis (vertical axis) value represents the false positive (false negative) rate, as shown in Figure 12. The horizontal axis represents the GT boxes, while the vertical axis represents the predicted boxes. The colors along the main diagonal are relatively darker and more concentrated, indicating that the model has good detection ability for each category in the COP dataset. In the non-diagonal areas, the colors are lighter, suggesting that there are very few instances where a certain category is incorrectly predicted as another category by the model. Hence, these areas appear almost white. And taking pedestrians as an example, the joint point of pedestrians on the horizontal and vertical axis, respectively, has a value of 0.93, indicating that 93% of the pedestrian labels are correctly predicted. And 1% of the pedestrian labels are incorrectly predicted as riders. Although there may be differences between Figure 12 and Table 2, that was because of using the training dataset and validation dataset separately, which leads to variations in category quantities and diversity. Moreover, Figures 9 and 10 have already demonstrated EAAnet's overall superiority in terms of detection performance and heat  
Figure 9: Detection effects. (**a**) GT. (**b**) Prediction of YOLOv5. (**c**) Prediction of EAAnet. maps. Therefore, through Figure 12, it is once again proven that our improvement work has enhanced EAAnet's perception capability for small crowd objects by effectively extracting fine-grained information. Figure 11: Performance difference for each class. (**a**) P curve. (**b**) R curve. (**c**) PR curve. (**d**) F1 curve. Figure 10: Heat map of some representative stages. (**a**) Ordinary environment. (**b**) Indoor crowd. (**c**) Lower feature map in dense environment. Although attention modules can be used to help improve model performance, their complexity leads to a tendency to overfit the data, resulting in poor performance in the prediction of unknown data. In order to ensure the model appropriately fits the COP dataset, the experimental hyper parameter fine-tuning (the specific hyperparameters related to the optimizer, loss function, image preprocessing and image enhancement) adopts evolution strategies, which use a genetic algorithm (GA) to select the more appropriate hyper parameters through the settings of the mutation scale, lower limit, and upper limit. For example, the Adam optimizer has an initial learning rate of 0.001 by default, which is fine-tuned to 0.01 through evolution strategies. Meanwhile, to prove the effectiveness of model improvement rather than false improvement caused by overfitting, we explored the model performance on the training dataset and validation dataset based on three types of losses, as shown in Figures 13-15. In these figures, with the increase in epoch number, both the training loss and validation loss of EAAnet showed a downward trend. And among all types of losses, the validation loss was lower than the training loss; it did not decrease first and then increase, indicating that EAAnet performed well on previously unseen data, and it did not overfit the details of COP data. In the figure, although EAAnet and YOLOv5s tend to converge to similar values in the end, the curve of EAAnet is smoother, indicating that the improved model is more robust to the deviation between samples.