Weiyu Hao1, Jingyi Wang2, Huimin Lu3,*
CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4419-4433, 2024, DOI:10.32604/cmc.2024.055478
- 19 December 2024
Abstract While traditional Convolutional Neural Network (CNN)-based semantic segmentation methods have proven effective, they often encounter significant computational challenges due to the requirement for dense pixel-level predictions, which complicates real-time implementation. To address this, we introduce an advanced real-time semantic segmentation strategy specifically designed for autonomous driving, utilizing the capabilities of Visual Transformers. By leveraging the self-attention mechanism inherent in Visual Transformers, our method enhances global contextual awareness, refining the representation of each pixel in relation to the overall scene. This enhancement is critical for quickly and accurately interpreting the complex elements within driving scenarios—a fundamental… More >
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