# An Approach to Incorporating Implicit Knowledge in Object Detection Models
## 4 Experiment
### The Experimental Results and Analysis
With an increase in the number of parameter dictionaries, there was also an increase in the latent knowledge stored within these dictionaries. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**mAP** & **Baseline** & **1** & **4** & **6** & **10** & **15** \\ \hline car & 0.612 & 0.613 & 0.614 & 0.615 & 0.614 & 0.615 \\ bus & 0.646 & 0.649 & 0.647 & 0.647 & 0.647 & 0.649 \\ van & 0.457 & 0.471 & 0.457 & 0.474 & 0.471 & 0.472 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The mAP values of categories in certain experimental groups. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**FPN**} & \multicolumn{2}{c}{**PAN**} & \multirow{2}{*}{**P**} & \multirow{2}{*}{**R**} & \multirow{2}{*}{**AP50**} & \multirow{2}{*}{**AP75**} & \multirow{2}{*}{**mAP**} \\  & **Conv** & **Upsample** & & & & & & \\ \hline Baseline & \(\bigcirc\) & \(\bigcirc\) & \(\bigcirc\) & \(\bigcirc\) & 0.731 & 0.669 & 0.721 & 0.68 & 0.571 \\
17 & \(\times\) & \(\times\) & \(\times\) & \(\times\) & 0.749 & 0.665 & 0.729 & 0.688 & 0.578 \\
18 &  
As shown in Table 4, when analyzing the information regarding model parameters and computational complexity, the incorporation of implicit knowledge resulted in an increase of approximately 0.12% in model parameters and an increase of 0.08% to 0.11% in computational complexity. However, there was a significant improvement in the model's performance. It can be observed from Figure 8 that adding dictionary parameters did not have a substantial impact on the model training. We compare the effectiveness of our proposed method with commonly used approaches in the field of object detection based on three aspects: mAP, computational complexity, and processing time per image; the experimental results are shown in Table 5. For the entire training process, we followed the YOLOv5 [17] training process. Furthermore, we opted to first train for 80 epochs and then fine-tune for an additional 40 epochs. In order to validate the effectiveness of our proposed method on other datasets, experiments were conducted using the same configuration on the KITTI dataset. The results indicate that our proposed method successfully integrates latent knowledge into object detection models. As shown in Table 6, compared to the baseline model, our proposed method effectively improves the relevant metrics of the object detection model.The performance of the proposed method on two datasets is shown in Figure 9. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **\# Parameters** & **MFLOPS** \\ \hline Baseline & 7,016,287 & 158,429.6371 \\ Implicit1  & 702,357 (+0.12\%) & 158,556.3808 (+0.08\%) \\ Implicit2 & 7,025,791 (+0.13\%) & 158,603.9097 (+0.11\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of model parameters and computational complexity. Figure 8: Model learning curve. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **mAP** & **Gflops** & **Time (ms)** \\ \hline CBAM [23] & 0.598 & 15.8 & 2.0 \\ MobileNetv2 [32] & 0.583 & 10.4 & 2.2 \\ MobileNet3 [33] & 0.559 & 37.9 & 2.3 \\ ShfruleNet [34] & 0.505 & 3.6 & 1.5 \\ Ours & 0.596 & 15.8 & 2.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of performance among different methods on UA-DETRAC dataset.