# Low-Resolution Target Detection with High-Frequency Information Preservation
## 4 Experiments
### Tiny-Object Detection  
Since tiny objects have limited pixel representation, they are particularly sensitive to high-frequency information loss. To further validate the effectiveness of our method, we conducted experiments on the TinyPersonv2 dataset, which focuses on tiny object detection tasks. As shown in the Table 3, single-stage models like YOLO perform significantly worse than two-stage models such as Faster R-CNN on TinyPersonv2. However, our method achieves a notable improvement of 2.7 AP and 1.3 AP (small) on the YOLO model compared to the marginal gain of 0.5 AP and 0.2 AP (small) achieved by SPD. This highlights the robustness and effectiveness of our method. Moreover, when integrated into the Faster R-CNN model, the SPD module even causes performance degradation. In contrast, our method achieves superior improvements in both mAP and AP (small), further demonstrating its effectiveness for detecting tiny objects.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Method** & **Backbone** & **Imgsize** & **mAP** & **AP(0.5)** & **AP(0.75)** \\ \hline HRNet [29] & HRNet-W32 & 256 \(\times\) 192 & 73.49 & 89.75 & 8.95 \\ HRNet + SPD [2] & HRNet-W32 & 256 \(\times\) 192 & 73.66 & 89.81 & 81.01 \\ HRNet + HFIP & HRNet-W32 & 256 \(\times\) 192 & 74.10 & 90.27 & 81.32 \\ HRNet [29] & HRNet-W32 & 128 \(\times\) 96 & 64.18 & 87.77 & 73.96 \\ HRNet + SPD [2] & HRNet-W32 & 128 \(\times\) 96 & 64.72 & 88.07 & 74.47 \\ HRNet + HFIP & HRNet-W32 & 128 \(\times\) 96 & 65.17 & 88.18 & 74.83 \\ HRNet [29] & HRNet-W32 & 64 \(\times\) 48 & 58.67 & 86.23 & 67.86 \\ HRNet + SPD [2] & HRNet-W32 & 64 \(\times\) 48 & 58.88 & 86.32 & 67.97 \\ HRNet + HFIP & HRNet-W32 & 64 \(\times\) 48 & 59.26 & 86.79 & 68.67 \\ \hline SimBa. [30] & ResNet-50 & 256 \(\times\) 192 & 70.74 & 89.45 & 78.58 \\ SimBa. + SPD [2] & ResNet-50 & 256 \(\times\) 192 & 71.41 & 89.43 & 79.54 \\ SimBa. + HFIP & ResNet-50 & 256 \(\times\) 192 & 71.70 & 89.55 & **79.39** \\ SimBa. [30] & ResNet-50 & 128 \(\times\) 96 & 57.16 & 85.14 & 64.82 \\ SimBa. + SPD [2] & ResNet-50 & 128 \(\times\) 96 & 59.62 & 86.36 & 68.40 \\ SimBa. + HFIP & ResNet-50 & 128 \(\times\) 96 & 60.38 & 86.60 & 69.26 \\ SimBa. [30] & ResNet-50 & 64 \(\times\) 48 & 30.88 & 69.55 & 22.36 \\ SimBa. + SPD [2] & ResNet-50 & 64 \(\times\) 48 & 35.98 & 74.65 & 29.93 \\ SimBa. + HFIP & ResNet-50 & 64 \(\times\) 48 & 36.87 & 75.56 & 31.94 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison with state-of-the-art human pose estimation methods under the low-resolution setting on the COCO val set.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Image Size** & **mAP** & **mAP (Small)** \\ \hline YOLO11n [27] & 640 \(\times\) 640 & 24.7 & 12.7 \\ YOLO11n + SPD [2] & 640 \(\times\) 640 & 25.2 & 12.9 \\ YOLO11n + HFIP & 640 \(\times\) 640 & 27.4 & 14.0 \\ \hline Faster R-CNN [25] & 640 \(\times\) 640 & 76.6 & 72.8 \\ Faster R-CNN + SPD [2] & 640 \(\times\) 640 & 76.3 & 71.9 \\ Faster R-CNN + HFIP & 640 \(\times\) 640 & 78.5 & 74.7 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance comparison on the TinyPersonv2 dataset.