触摸路面和斑马交叉路口的实时细分,用于使用嵌入式视觉传感器的视觉障碍者辅助
Yiqiang Jiang1, Shicheng Yan2, Jianyang Liu1
1School of Mechanical and Electrical Engineering, Yibin University, Yibin 644000, China.
Sensors (Basel, Switzerland)
|February 13, 2026
概括
这项研究介绍了一种用于视力障碍者导航的轻量级AI模型,在智能引导机器人上准确识别触觉路面和斑马十字路口. 该系统在嵌入式平台上使用先进的计算机视觉来提高视力受损者的安全性和流动性.
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:
- 视障人士面临着重大的安全和移动性挑战.
- 现有的辅助技术往往缺乏有效导航所需的精度和实时性能.
- 准确检测关键的环境线索,如触觉铺路和斑马穿越,对于独立旅行至关重要.
研究的目的:
- 开发一种轻量级,高精度的语义细分网络,用于识别触觉路面和斑马交叉路口.
- 在智能引导机器人上部署这个网络,以便在现实世界中应用.
- 在资源受限的嵌入式系统上增强实时性能和细分精度.
主要方法:
- 设计了一个改进的G-GhostNet骨干,以实现高效的功能提取.
- 集成了一个深度可分离的基于卷积的坐标注意模块.
- 使用重新设计的Atrous空间金字塔聚合 (ASPP) 模块进行多尺度特征捕获.
- 采用专门的解码器来实现有效的多层次功能融合.
主要成果:
- 在触摸路面细分方面实现了97%的mPA和94%的mIoU.
- 在斑马交叉细分方面实现了93%的mPA和86%的mIoU.
- 达到了59.2fps的推断速度,超过了主流语义细分网络.
结论:
- 拟议的语义细分网络显示了重要的导航辅助工具的高精度和实时性能.
- 该方法已被验证用于嵌入式系统的实际使用,显著有利于视力受损人士.
- 这项研究有助于推进辅助技术,以提高移动性和安全性.
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