## 3 Proposed Method
Future research will focus on improving multi-scale feature interactions and further integrating attention mechanisms to balance model performance and computational efficiency in semantic segmentation tasks. Conceptualization, J.T. and P.X. ; Methodology, J.T. and P.X. ; Software, J.T. ; Validation: J.T., P.X. and X.B. ; Formal analysis, J.T. and X.B. ; Investigation, J.T., P.X. and X.B. ; Resources, P.X., Z.X. and N.L. ; Data curation, J.T. and X.B. ; Writing--original draft preparation, J.T. ; Writing--review and editing, J.T., P.X., Z.X. and N.L. ; Visualization, J.T. and X.B. ; Supervision, Z.X. and N.L. ; Project administration, Z.X. and N.L. ; All authors have read and agreed to the published version of the manuscript. This research was funded by the Department of Science and Technology of Jilin Province grant number 20230401104 YY. Not applicable. The datasets utilized in this study are publicly available and can be accessed as follows: 1. Cityscapes: [[https://www.cityscapes-dataset.com/](https://www.cityscapes-dataset.com/)]([https://www.cityscapes-dataset.com/](https://www.cityscapes-dataset.com/)); (accessed on 24 July 2025) 2. CamVid: [[http://mi.eng.cam.ac.uk/research/projects/VideoRec/CamVid/](http://mi.eng.cam.ac.uk/research/projects/VideoRec/CamVid/)]([http://mi.eng.cam.ac.uk/research/projects/VideoRec/CamVid/](http://mi.eng.cam.ac.uk/research/projects/VideoRec/CamVid/)); (accessed on 24 July 2025) 3. ADE20K (MIT Scene Parsing Benchmark): [[http://sceneparsing.csail.mit.edu/](http://sceneparsing.csail.mit.edu/)]([http://sceneparsing.csail.mit.edu/](http://sceneparsing.csail.mit.edu/)) (accessed on 24 July 2025). The authors declare no conflicts of interest. ## References

* [PERSON] et al. (2019) [PERSON]; [PERSON]; [PERSON]; [PERSON] Supplementary Material of Learning Lightweight Lane Detection CNNs by Self Attention Distillation. In Proceedings of the International Conference on Computer Vision (ICCV), Seoul, Republic of Korea, 27 October-2 November 2019. Available online: [[http://www.xueshufan.com/publication/2981441441](http://www.xueshufan.com/publication/2981441441)]([http://www.xueshufan.com/publication/2981441441](http://www.xueshufan.com/publication/2981441441)) (accessed on 15 October 2024). * [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] A Comparative Study of Real-Time Semantic Segmentation for Autonomous Driving: In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2018. Available online: [[http://www.semanticscholar.org/paper/f553b191501a7](http://www.semanticscholar.org/paper/f553b191501a7) bb4a33284f2 fe94 ddd3 bc1 db6d0]([http://www.semanticscholar.org/paper/f553b191501a7](http://www.semanticscholar.org/paper/f553b191501a7) bb4a33284f2 fe94 ddd3 bc1 db6d0) (accessed on 15 October 2024). * [PERSON] et al. (2015) [PERSON]; [PERSON]; [PERSON] Fully Convolutional Networks for Semantic Segmentation. _IEEE Trans. Pattern Anal. Mach. Intell._**2015**, 39, 640-651. Available online: [[http://arxiv.org/abs/1411.4038](http://arxiv.org/abs/1411.4038)]([http://arxiv.org/abs/1411.4038](http://arxiv.org/abs/1411.4038)) (accessed on 16 October 2024). * [PERSON] (2015) [PERSON]; [PERSON] [PERSON]; [PERSON] U-Net: Convolutional Networks for Biomedical Image Segmentation. In Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany, 5-9 October 2015; pp. 234-241. [CrossRef]
* [PERSON] et al. (2017) [PERSON]; [PERSON]; [PERSON] [PERSON] SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation. _IEEE Trans. Pattern Anal. Mach. Intell._**2017**, 39, 2481-2495. [CrossRef] [PubMed]
* [PERSON] et al. (2021) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] SegFormer: Simple and efficient design for semantic segmentation with transformers. _Adv. Neural Inf. Process. Syst._**2021**, 34, 12077-12090. * [PERSON] et al.