# Swin-APT: An Enhancing Swin-Transformer Adaptor for Intelligent Transportation
## 6 Conclusions  
This work focuses on deep learning-based semantic segmentation and object detection for intelligent transportation systems. Swin-APT, a lightweight network and an adapter network suitable for image semantic segmentation and object detection tasks is designed. Additionally, a module based on inter-frame consistency of images is proposed, which allows the full utilization of the consistency of adjacent frame information to extract more accurate road information from images. The adapter network is applied to the multi-scale feature space, which can effectively identify scene targets of different scales. The proposed method is verified on four datasets: BDD100K, CamVid, SYNTHIA, and CeyMo, and outperforms the baseline by 13.1%. Furthermore, the experimental results on the road marking detection benchmark show an improvement of 1.85% of mAcc.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{} & \multicolumn{3}{c}{**Module**} & \multicolumn{8}{c}{**Category**} \\ \hline apt & InCM & MS & BL & CL & DM & JB & LA & PC & RA & SA & SL & SLA & SRA & mAP \\ \hline ✗ & ✗ & ✗ & 90.7 & 78.6 & 87.4 & 83.7 & 69.9 & 85.6 & 68.2 & 86.8 & 67.9 & 89.1 & 62.7 & 79.1 \\ ✓ & ✗ & ✗ & 91.8 & 81.1 & 89.5 & 85.8 & 71.3 & 87.9 & 69.8 & 87.5 & 69.3 & 90.0 & 64.9 & 80.8 \\ ✓ & ✓ & ✓ & 92.4 & 81.8 & 90.7 & 86.1 & 71.6 & 88.4 & 70.5 & 88.1 & 70.9 & 90.4 & 65.6 & **81.5** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison results of mAP on the CeyMo dataset. Numbers in bold represents the best performance.  
Figure 5: Visualization of road marking detection.  
**Author Contributions:** Conceptualization, C.W.; Investigation, C.W.; Methodology, Y.L.; Resources, Y.Z.; Software, Y.L.; Supervision, S.Z.; Visualization, Y.Z.; Writing--original draft, K.C.; Writing--review and editing, Y.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported in part by the National Natural Science Foundation of China under Grant 62272089 and the General Program of Science and Technology Department of Sichuan Province under Grant 2022 YFG0207.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are openly available. BDD100K is available at [[https://bdd-data.berkeley.edu/](https://bdd-data.berkeley.edu/)]([https://bdd-data.berkeley.edu/](https://bdd-data.berkeley.edu/)) (accessed on 19 November 2023), reference [21]; CamVid is available at [[https://doi.org/10.1016/j.patrec.2008.04.005](https://doi.org/10.1016/j.patrec.2008.04.005)]([https://doi.org/10.1016/j.patrec.2008.04.005](https://doi.org/10.1016/j.patrec.2008.04.005)), reference [22]; SYNTHIA is available at adas.cvc.uab.es/synthiain (accessed on 19 November 2023), reference [23]; CeyMo is available at [[https://github.com/oshadajay/CeyMo](https://github.com/oshadajay/CeyMo)]([https://github.com/oshadajay/CeyMo](https://github.com/oshadajay/CeyMo)) (accessed on 19 November 2023), reference [24].  
**Conflicts of Interest:** The author declares no conflict of interest.