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Updated: May 12, 2025

Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss

Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss

Published on: April 11, 2025

利用基于视网网膜的对象检测模型来协助视障人士使用元启发式优化算法.

Alaa O Khadidos¹, Ayman Yafoz^2,3

¹Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia.

Scientific reports

|May 9, 2025

概括

本研究引入了一种新的物体检测模型 (ODMVII-MOA),以帮助视力障碍者完成日常任务. 该模型在识别物体方面达到99.69%的准确性,显著改善了视力受损者辅助技术.

关键词:

计算机视觉计算机视觉计算机视觉花优化器花优化器深度学习是一种深度学习.功能提取功能提取对象检测检测对象检测对象检测视力受损的个人视力受损的个人

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相关实验视频

Last Updated: May 12, 2025

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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:

视障人士在日常活动中面临重大挑战.
现有的物体检测方法需要改进,以便在辅助技术中的实际应用.
基于深度学习的对象识别为增强检测提供自主特征提取.

研究的目的:

为视觉障碍者提供一个新的物体检测模型,采用元启发式优化算法 (ODMVII-MOA).
增强视力障碍者实时物体检测和识别能力.
为了提高对象检测在具有挑战性的条件如低光和遮蔽的准确性和可靠性.

主要方法:

使用韦纳波器 (WF) 进行图像预处理,以减少噪声.
使用RetinaNet技术进行物体检测.
使用EfficientNetB0进行特征提取,并使用LSTM-Autoencoder (LSTM-AE)进行分类.
通过花优化器 (DO) 来优化LSTM-AE的超参数优化.

主要成果:

在物体检测任务中,ODMVII-MOA模型表现出卓越的性能.
在室内数据集上的实验验证显示了高准确度.
获得了99.69%的分类准确率,超过了现有方法.

结论:

拟议的ODMVII-MOA技术显著提高对象检测的视力障碍者.
集成先进的深度学习和元启发式优化产生了最先进的结果.
这个模型有望为视力受损者开发更有效的导航和物体识别工具.