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DSC-Net:使用双分支Swin-CNN架构使用视觉传感器增强盲路语义细分

Ying Yuan¹, Yu Du¹, Yan Ma¹

¹Beijing Key Laboratory of Information Service Engineering, College of Robotics, Beijing Union University, Beijing 100101, China.

Sensors (Basel, Switzerland)

|September 28, 2024

概括

本研究介绍了双分支天-CNN网络 (DSC-Net) 进行改进的盲路语义细分. 通过准确识别道路特征和边界,DSC-Net 增强了视力受损人士的导航能力.

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:

视觉传感器对于城市导航系统至关重要,特别是对于视力受损者来说.
盲道的语义细分在提取全球上下文和边缘特征方面面临挑战.
现有的卷积神经网络 (CNN) 与全球背景和不连续的特征检测扎.

研究的目的:

开发一种先进的方法,准确地对盲目道路进行语义细分.
为了提高对复杂的城市环境的理解,用于导航系统.
改进道路边界和封闭物体的检测.

主要方法:

推出了双分支Swin-CNN Net (DSC-Net),集成了Swin-Transformer和U-Net架构.
采用空间混合模块 (SBM) 来减少物体封闭造成的模糊.
在反向剩余模块 (IRM) 中利用混合注意力模块 (HAM) 进行边界利和处理速度.

主要成果:

在专业的盲路数据集上实现了97.72%的欧盟平均交叉点 (mIoU).
在额外的公共数据集上表现出卓越的表现.
成功增强了全球上下文和盲道边缘特征的提取.

结论:

DSC-Net有效地克服了传统CNN在盲目路语义细分方面的局限性.
拟议的方法在具有挑战性的道路场景中显著提高了准确性和边界检测.
DSC-Net为增强导航辅助技术提供了一个有前途的解决方案.

关键词:

盲目道路的细分盲目道路的细分边缘信息边缘信息语义细分语义细分语义细分语义细分变压器变压器变压器变压器视觉传感器视觉传感器

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