Deep-Learning-Based Visual Aid for Low Vision

doi:10.1109/LES.2025.3548959

2025-12-01

PRE

Rodolfo Bonnin *

Claudio Delrieux

María Fabiana Piccoli

DOI：10.1109/LES.2025.3548959

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摘要

En 中文

低视力对日常导航产生显著影响，影响全球约2.95亿人（Bourne et al., 2021）。尽管头戴式显示器（以下简称HMDs）在近几十年中被用于缓解这些限制，但据我们所知，目前尚无低成本现有系统利用头戴式显示器中的实时目标检测，专门为视障人士提供导航辅助。本文介绍了在资源受限的嵌入式平台上开发并评估了一种实时导航辅助设备，该设备能够自动检测并突出显示视野中的视觉兴趣点。我们利用了适用于树莓派5执行的最先进YOLO模型（v8、v9和v10），并通过优化的嵌入式系统推理引擎，研究了准确性、速度和功耗之间的权衡。使用自建数据集和公开可用的基准文件，我们的实验结果表明，在低成本、便携式设备上实现具有可接受精度的实时性能是可行的，从而为视障人士提供实用的语义感知辅助技术。

Keyword:

Measurement

Training

Navigation

YOLO

Power demand

Engines

Graphics processing units

Batteries

Real-time systems

Accuracy

Assistive technologies

computer vision

embedded systems

object detection

期刊

IEEE Embedded Systems Letters

IF:

论文数:

101

被引数:

696

机构

national university of the south

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2.3K

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被引数: 2

Universidad Nacional de San Luis

学者数:

论文数: 6

被引数: 0

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