# An Approach to Incorporating Implicit Knowledge in Object Detection Models
## 4 Experiment
### The Experimental Results and Analysis  
In the comparative experiment results, as shown in Table 1, there are four feature fusion operations in the model. We categorized them into two groups based on how the feature maps from two parts are processed: the FPN part and the PAN part. We performed addition or multiplication operations with feature maps and parameter dictionaries processed differently, or we did not make any modifications. When comparing the experimental group with the baseline model indicators, it can be observed that incorporating implicit knowledge can enhance the effectiveness of the object detection model. In the FPN part, we performed both addition and multiplication operations separately on the same feature fusion region. When comparing the AP50, AP75, and mAP in experiments 3 and 4, introducing implicit knowledge into the model using a multiplication  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**FPN**} & \multicolumn{2}{c}{**PAN**} & \multicolumn{2}{c}{**P**} & \multicolumn{2}{c}{**R**} & \multicolumn{1}{c}{**AP50**} & \multicolumn{1}{c}{**AP75**} & \multicolumn{1}{c}{**mAP**} \\  & **Conv1** & **Upsample1** & **Conv1** & **Conv** & & & & & \\ \hline Baseline & \(\bigcirc\) & \(\bigcirc\) & & \(\bigcirc\) & 0.731 & 0.669 & 0.721 & 0.68 & 0.571 \\
1 & \(\bigcirc\) & \(+\) & & \(\bigcirc\) & 0.73  
operation in the convolution part resulted in improvements of 0.3%, 0.4%, and 0.2%, respectively, when compared to using the addition operation. Similarly, when comparing the AP indicators in experiments 1 and 2 in the upsampling part, introducing implicit knowledge into the model using the addition operation resulted in improvements of 0.3%, 0.4%, and 0.2%, respectively, compared to using the multiplication operation. Likewise, in the PAN part, when comparing the AP indicators in experiments 9 and 10 and experiments 11 and 12, we can conclude that, for the feature fusion stage after the convolutional operation, using a multiplication operation for the parameter dictionary yields better results than using an addition operation in this dataset scenario. Therefore, we hypothesize that, for the feature fusion stage after linear operation, using the addition operation for the parameter dictionary yields better results than using the multiplication operation in this dataset scenario. Based on the conclusions above, we compared the results of the following experiments: experiments 6 and 8, experiments 6 and 7, and experiments 15 and 16. We found that the results were consistent with the hypothesis, indicating that linear interpolation operations for implicit knowledge fusion should use an addition operation, while convolution operations for implicit knowledge fusion should use a multiplication operation. Table 2 records the mAP (mean average precision) of three classes of targets in the experimental groups with significantly higher improvements compared to the baseline. When compared to the baseline, the integration of latent knowledge resulted in improved mAP values for all three categories of labels. In the dataset, the number of labels for the \"van\" category is significantly lower than those for the \"car\" and \"bus\" categories. The incorporation of latent knowledge notably enhanced the mAP for the \"van\" category, showing a 1.5% improvement in Experiment 15. This improvement suggests that the parameter dictionary successfully stored complex patterns existing in the data but not captured by the model, thereby enhancing the model's expressive capacity. Further experiments were conducted, building upon the conclusions drawn from the previous sections, as shown in Table 3. The enhancement of model AP metrics by latent knowledge became more pronounced. In Experiment 22, the feature maps obtained through convolution were multiplied by the parameter dictionary, while the feature maps obtained through upsampling were added by the parameter dictionary. As a result, AP50, AP75, and mAP were improved by 1.1%, 1%, and 1%, respectively, compared to the baseline.