# Swin-APT: An Enhancing Swin-Transformer Adaptor for Intelligent Transportation
## 5 Experiments
### Road Marking Detection
#### 5.2.2 Results on CeyMo  
To verify the robustness of the proposed model, the results of Swin-APT on the CeyMo [24] dataset are presented. To ensure consistency with ref. [24], a comparison is made with SSD-MobileNet-v1 [34; 35], SSD-Inception-v2 [34; 36], Mask-RCNN-Inception-v2 [36; 37], and Mask-RCNN-ResNet50 [25; 37]. The F1-Score and Macro F1-Score for each category are reported in Table 6.  
Table 6 provides a comparison of the Macro F1-Score results for various methods, including SSD MobileNet-v1, SSD Inception-v2, Mask-RCNN Inception-v2, Mask-RCNN ResNet50, and Swin-APT. Swin-APT achieves the highest Macro F1-Score of 90.18%, outperforming the other methods. Swin-APT demonstrates superiority over various other architectures, including SSD MobileNet-v1, SSD Inception-v2, Mask-RCNN Inception-v2, and Mask-RCNN ResNet50, in most of the evaluated categories. Notably, Swin-APT excels in categories such as \"Cycle Line\", \"Diamond\", \"Junction Box\", \"Left Arrow\", \"Right Arrow\", \"Straight Arrow\", \"Slow\", \"Straight-Left Arrow\", and \"Straight-Right Arrow\". Swin-APT consistently achieves high F1-Scores across a range of object categories, contributing to its excellent Macro F1-Score. This suggests that Swin-APT is effective in accurately detecting and classifying objects across diverse classes. The Macro F1-Score for Swin-APT (90.18%) is the highest among all methods in the comparison, indicating its overall superiority in terms of object detection accuracy.  
Similarly, the AP and overall mAP metrics of the lightweight Swin-L and Swin-APT for 11 object categories on the CeyMo [24] dataset are presented in Table 7, to verify the effectiveness of the multi-scale adapter network and the information metric comparison module for inter-frame consistency. Table 7 illustrates that Swin-APT exhibits a clear performance advantage over other configurations in terms of mAP on the CeyMo dataset.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Category**} & \multicolumn{5}{c}{**Methods**} \\ \cline{2-6}  & **SSD** & **SSD** & **Mask-RCNN** & **Mask-RCNN** & **Swin** \\  & **MobileNet-v1** & **Inception-v2** & **Inception-v2** & **ResNet50** & **APT** \\ \hline Bus Line & 98.00 & **100.00** & 93.33 & 91.26 & 97.46 \\ Cycle Line & 95.00 & 89.47 & 87.18 & 92.31 & **95.84** \\ Diamond & 87.82 & 88.58 & 92.05 & 91.05 & **93.90** \\ Junction Box & 82.50 & 90.70 & 92.13 & **96.63** & 95.05 \\ Left Arrow & 66.67 & 73.97 & 59.70 & 74.36 & **76.56** \\ Pedestrian Crossing & 94.95 & 95.44 & 96.72 & **96.86** & 95.97 \\ Right Arrow & 75.64 & 81.93 & 84.75 & 90.40 & **91.31** \\ Straight Arrow & 73.51 & 77.39 & 86.00 & 88.33 & **90.39** \\ Slow & 88.46 & 90.20 & 92.59 & 94.34 & **94.67** \\ Straight-Left Arrow & 65.22 & 65.93 & 84.55 & 89.47 & **90.32** \\ Straight-Right Arrow & 62.50 & 58.06 & **74.29** & 66.67 & 70.49 \\ Macro F1-Score & 80.93 & 82.88 & 85.75 & 88.33 & **90.18** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison results of Macro F1-Score on the CeyMo dataset. Numbers in bold represents the best performance.  
The proposed combination of modules plays a pivotal role in improving road marking detection accuracy across different categories.