This study aims to address the problem of semantic segmentation in complex road scenes, which has significant applications in fields such as autonomous driving, traffic monitoring, and urban planning. The methods investigated in our research primarily include key steps such as data collection, preprocessing, and annotation. We employ CNN models for data augmentation and introduce the DAFormer semantic segmentation algorithm. In the end, this paper proposes an enhanced DAFormer network architecture, incorporating techniques such as rare class sampling, Object Category ImageNet Feature Distance (FD), and learning rate warm-up. The application of these techniques enables DAFormer to better understand image content in complex road scenarios, providing a powerful tool to tackle real-world challenges. We evaluate its performance in this challenging task by comparing it with four traditional algorithms. Experimental results demonstrate a significant performance improvement in the enhanced DAFormer algorithm in complex road environments, achieving an average intersection over union (MIoU) of 0.82, pixel accuracy (PA) of up to 89%, and improved timeliness. Compared to other algorithms, the enhanced DAFormer exhibits superior performance in terms of accuracy, stability, and timeliness.

Zhaoxiang Wang, Kaiqi Huang. Road Scene Semantic Segmentation Based on Deep Learning. International Journal of Frontiers in Engineering Technology (2023), Vol. 5, Issue 11: 86-93. https://doi.org/10.25236/IJFET.2023.051113.

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