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CSANet:RGB-T城市场景理解的上下文空间意识网络
Ruixiang Li1, Zhen Wang1,2, Jianxin Guo1
1School of Electronic Information, Xijing University, Xijing Road, Chang'an District, Xi'an 710123, China.
Journal of imaging
|June 25, 2025
概括
通过使用RGB和热红外数据,CSANet改善了自动驾驶的语义细分. 这种上下文空间意识网络 (CSANet) 提高了在低光和恶劣天气等具有挑战性的条件下的性能.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器人技术 机器人技术 机器人技术
背景情况:
- 语义细分对于理解自动驾驶中的城市场景至关重要.
- 现有的方法在低光和恶劣天气下扎,限制了现实世界的应用.
- 整合RGB和热红外 (TIR) 数据提供了一个有前途的解决方案.
研究的目的:
- 开发一个新的框架,CSANet,用于强大的RGB-T语义细分.
- 增强特征提取和融合,在具有挑战性的条件下提高精度.
- 推进自动驾驶系统的能力.
主要方法:
- CSANet使用高效的编码器进行本地和全球特征提取.
- 一个层次融合策略选择性地整合视觉和语义信息.
- 关键模块包括频道空间交叉融合 (CSCFM),多头融合 (MHFM) 和空间坐标注意力 (SCAM).
主要成果:
- 在基准数据集 (MFNet,PST900) 上,CSANet 展示了最先进的性能.
- 该框架有效地融合了RGB和TIR模式,以实现高级语义细分.
- 观察到对象定位精度的显著改善.
结论:
- CSANet为RGB-T语义细分提供了一个强大的解决方案,特别是在不利的条件下.
- 拟议的融合策略和注意力机制增强了对复杂城市环境的理解.
- 这项工作有助于更安全,更可靠的自动驾驶系统.
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