# TIG-DETR: Enhancing Texture Preservation and Information Interaction for Target Detection
## 4 Results
### Main Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Method** & **Backbone** & **Schedute** & **AP** & **AP\({}_{\text{80}}\)** & **AP\({}_{\text{73}}\)** & **AP\({}_{\text{S}}\)** & **AP\({}_{\text{9}}\)** & **AP\({}_{\text{1}}\)** \\ \hline Faster R-CNN * & ResNet-50-FPN & \(\times\)1 & 36.4 & 58.1 & 39.1 & 21.3 & 40.5 & 44.6 \\ Faster R-CNN * & ResNet-101-FPN & \(\times\)1 & 38.6 & 60 & 42.1 & 22.2 & 42.5 & 47.1 \\ Faster R-CNN * & ResNet-101-FPN & \(\times\)2 & 39.4 & 61.1 & 43.2 & 22.6 & 42.7 & 50.1 \\ Faster R-CNN * & ResNet-101-23404-FPN & \(\times\)1 & 40.3 & 62.6 & 43.6 & 24.5 & 42.9 & 49.9 \\ Faster R-CNN * & ResNet-101-62404-FPN & \(\times\)1 & 41.7 & **64.9** & 44.4 & 24.7 & 45.8 & 51.3 \\ Mask R-CNN * & ResNet-50-FPN & \(\times\)1 & 37.1 & 58.9 & 40.3 & 22.3 & 40.5 & 45.5 \\ Mask R-CNN * & ResNet-101-FPN & \(\times\)1 & 39.1 & 61.2 & 42.2 & 22.8 & 42.3 & 49.2 \\ Mask R-CNN * & ResNet-101-FPN & \(\times\)2 & 40 & 61.8 & 43.7 & 22.7 & 43.4 & 52.1 \\ RetinaNet * & ResNet-50-FPN & \(\times\)1 & 35.8 & 55.7 & 38.7 & 19.4 & 39.7 & 44.9 \\ RetinaNet * & MobileNet-50-FPN & \(\times\)1 & 32.9 & 52.1 & 34.9 & 17.9 & 34.8 & 42.6 \\ DETR & ResNet-50 & \(\times\)1 & 42 & 62.4 & 44.2 & 20.5 & 45.8 & **61.1** \\ Deformable DETR & ResNet-50 & \(\times\)1 & 43.8 & 62.6 & 47.7 & 26.4 & 47.1 & 58 \\ Deformable DETR+IAM & ResNet-50 & \(\times\)1 & **44.3** & 62.9 & **48.3** & **26.3** & **47.6** & 60.3 \\ \hline Faster R-CNN * (ours) & ResNet-50-TE-FPN & \(\times\)1 & 38.4 & 61 & 41.9 & 23.1 & 41.7 & 47.5 \\ Faster R-CNN (ours) & ResNet-101-TE-FPN & \(\times\)1 & 40.2 & 62.6 & 43.6 & 23.5 & 43.5 & 50.9 \\ Faster R-CNN (ours) & ResNet-101-TE-FPN & \(\times\)2 & 41.1 & 63.4 & 44.3 & 23.6 & 44.1 & 52.7 \\ Faster R-CNN (ours) & ResNet-101-32404-TE-FPN & \(\times\)1 & 41.5 & 63.8 & 45.1 & 24.8 & 45.1 & 52.3 \\ Faster R-CNN (ours) & ResNet-101-64x4d-TE-FPN & \(\times\)1 & 42.7 & 65.4 & 46 & 25.9 & 45.9 & 53.5 \\ Mask R-CNN (ours) & ResNet-101-TE-FPN & \(\times\)1 & 38.9 & 61.1 & 42.4 & 23.2 & 42.2 & 49 \\ Mask R-CNN (ours) & ResNet-101-TE-FPN & \(\times\)1 & 40.4 & 63 & 44.2 & 23.7 & 43.3 & 51.4 \\ Mask R-CNN (ours) & ResNet-101-TE-FPN & \(\times\)2 & 41.5 & 63.6 & 45.7 & 24.4 & 41.4 & 53.2 \\ RetinaNet (ours) & ResNet-50-TE-FPN & \(\times\)1 & 36.9 & 57.9 & 39.6 & 20.8 & 40.1 & 46.4 \\ RetinaNet (ours) & MobileNet-v2-TE-FPN & \(\times\)1 & 33.9 & 53.7 & 35.8 & 18.5 & 35.7 & 43.9 \\ TIG-DETR & ResNet-50 & \(\times\)1 & 43.1 & 62.1 & 46.2 & 24.7 & 46.8 & 60.5 \\ TIG-DETR & ResNet-50-TE-FPN & \(\times\)1 & **44.1** & **62.8** & **48.4** & **25.6** & **47.9** & **62.4** \\ \hline \hline \end{tabular}  
* We compared our TIG-DERE and TE-FPN with some advanced target detection networks and FPN networks in the COCO dataset for our experiments. The symbol ‘\(*\)’ means our re-implemented results throughdetection. The bolded part of the font indicates the largest indicator in the comparison experiment.  
\end{table} 
Table 1: Comparison with baseline and state-of-the-art COCO test development methods. Figure 6: TIG-DETR vs. DETR. red bounding box shows the detection result of DETR, while blue bounding box shows the detection result of TIG-DETR. By replacing FPN with TE-FPN, we achieved an AP of 38.4 for Faster R-CNN using ResNet-50 as the backbone, which is 2.0 points higher than Faster R-CNN based on ResNet-50-FPN. Moreover, TE-FPN also performs well with more powerful backbone networks. For instance, when using ResNet-101-32x4d, our approach improves the AP by an additional 1.2 points. Table 1 demonstrates the varying degrees of performance improvement achieved by TE-FPN across different backbones, detectors, and tasks, highlighting its robustness and generalization capability. Visualization results are presented in Figure 7.