障碍物检测系统用于导航 基于深度学习技术的视障人士的导航辅助
Yahia Said1,2,3, Mohamed Atri4, Marwan Ali Albahar5
1Remote Sensing Unit, College of Engineering, Northern Border University, Arar 91431, Saudi Arabia.
Sensors (Basel, Switzerland)
|June 10, 2023
概括
这项研究引入了一个智能导航系统,用于视力障碍者,增强移动性和社会融合. 一种快速的神经架构搜索 (NAS) 方法优化了对象检测模型,提高了性能,以获得更好的导航辅助.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 辅助技术 辅助技术 辅助技术
背景情况:
- 视障人士面临着影响社会融合和生活质量的移动性挑战.
- 个人导航系统对于提高独立性和信心至关重要.
- 深度学习和神经架构搜索 (NAS) 为先进的导航解决方案提供了潜力.
研究的目的:
- 为视力受损者开发智能导航辅助系统.
- 提出一种高效的NAS技术,用于优化计算机视觉中的对象检测模型.
- 为解决NAS在现实世界应用中的计算局限性.
主要方法:
- 开发了一种新的,快速的NAS方法,使用定制的强化学习技术.
- 该NAS方法专注于优化特征金字塔网络和预测阶段的无物体检测模型.
- 该模型在Coco和室内物体检测和识别 (IODR) 数据集上进行了训练和评估.
主要成果:
- 拟议的NAS方法有效地寻找了最佳的对象检测框架.
- 结果的模型显示,与原始模型相比,平均精度 (AP) 提高了2.6%.
- 优化模型在可接受的计算复杂度下实现了这种改进.
结论:
- 开发的智能导航系统有望改善视力受损者的流动性和生活质量.
- 提出的快速NAS技术对于计算机视觉中的自定义对象检测任务是有效和高效的.
- 这项研究有助于通过高效的深度学习架构优化来推进辅助技术.
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