# Low-Resolution Target Detection with High-Frequency Information Preservation
## 4 Experiments
### Ablation Study
#### 4.6.2 Effect of High-Frequency Information Preservation (HFIP) in Different Layers  
We posit that the problem of high-frequency information loss does not occur across all layers in convolutional neural networks. Therefore, we validated the effect of HFIP applied in different network layers on two tasks: object detection and human pose estimation. As can be observed from Table 5, applying HFIP to the entire network (YOLOv5s + HFIP-A) leads to the highest performance. However, YOLOv5s + HFIP-B, which just applies HFIP to the stem stage of the network, only requires a 58.5% computational cost but achieves 97.25% performance in mAP and 99.7% performance in mAP (small). Meanwhile, a similar finding can be obtained in the human pose estimation task, as can be observed from Table 6. However, the integration of HFIP into non-stem layers (HFIP-C) results in only marginal improvements in object detection and human pose estimation tasks, accompanied by a significant computational cost.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **mAP** & **AP(0.5)** & **AP(0.75)** & **Param** & **FLOPs** \\ \hline SimBa. [30] & 57.16 & 85.14 & 64.82 & 34.00 M & 1.37 G \\ SimBa. + HFIP-A & 60.61 & 86.86 & 69.57 & 39.10 M & 1.90 G \\ SimBa. + HFIP-B & 60.38 & 86.60 & 69.26 & 34.03 M & 1.62 G \\ SimBa. + HFIP-C & 57.23 & 85.45 & 65.12 & 38.80 M & 1.75 G \\ \hline \hline \end{tabular}
\end{table}
Table 6: Effect of high-frequency information preservation (HFIP) at different layers on human pose estimation in low-resolution (128 \(\times\) 96) settings. (A: whole network; B: stem only; C: non-stem layers).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**DFACC** & **DHFL** & **mAP** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]** \\ \hline \multirow{4}{*}{\(\surd\)} & & 58.90 & 85.12 & 67.71 \\  & & 59.36 & 85.67 & 68.12 \\  & \(\surd\) & 60.05 & 86.23 & 68.75 \\ \(\surd\) & \(\surd\) & 60.38 & 86.60 & 69.26 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Effect of dilated frequency-aware channel correlation (DFACC) and decoupled high-frequency learning (DHFL) on the COCO val set.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **mAP** & **mAP (Small)** & **Param** & **FLOPs** \\ \hline YOLOv5s [26] & 37.70 & 21.09 & 7.20 M & 8.31 G \\ YOLOv5s + HFIP-A & 41.85 & 24.38 & 8.10 M & 17.90 G \\ YOLOv5s + HFIP-B & 40.70 & 23.40 & 7.26 M & 10.47 G \\ YOLOv5s + HFIP-C & 40.03 & 21.90 & 8.04 M & 16.74 G \\ \hline \hline \end{tabular}
\end{table}
Table 5: Effect of high-frequency information preservation (HFIP) in different layers on object detection in low-resolution (640 \(\times\) 640) settings. (A: whole network; B: stem only; C: non-stem layers).  
The aforementioned experimental results indicate that high-frequency information loss does not occur to the same extent across the entire network but is more concentrated in the stem stage. Replacing all downsampling operations in the network with the HFIP module (HFIP-A) significantly mitigates high-frequency information loss and leads to substantial performance improvements. However, this also results in a considerable increase in computational overhead. Therefore, we recommend the HFIP-A configuration for scenarios where performance is prioritized over real-time requirements. For a better balance between performance and efficiency, we suggest using the HFIP-B configuration, which is also adopted in all other experiments for consistency.