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使用基于深度视觉处理算法的导航结果在第二代超状视网膜假体中

Lauren Moussallem¹, Lisa Lombardi¹, Myra B McGuinness^1,2

¹Centre for Eye Research Australia, Royal Victorian Eye & Ear Hospital, Melbourne, VIC, Australia.

Journal of neural engineering

|April 2, 2025

概括

新的局部背景封闭 (LBE) 视觉处理 (VP) 方法在实验室和现实世界的测试中与Lanczos2 (L2) 方法相比,显著改善了视网膜假体用户的障碍物检测.

科学领域:

生物医学工程生物医学工程
眼科医生眼科眼科
计算机视觉计算机视觉

背景情况:

视网膜假体接受者由于视力受损,经常面临导航方面的挑战.
现有的视觉处理 (VP) 方法可能无法完全恢复环境感知.
需要新的VP算法来增强植入物用户的空间意识和移动性.

研究的目的:

为了比较新的基于深度的VP方法,局部背景封闭 (LBE) 的有效性,与标准方法,Lanczos2 (L2) 相比.
在受控实验室环境和复杂的现实环境中评估性能.
评估VP方法对超状视网膜假肢使用者的障碍物检测和导航的影响.

主要方法:

使用LBE和L2 VP方法测试了四名带有超状视网膜假肢的参与者.
实验室试验涉及到在白色走廊上与随机的障碍物进行导航.
现实世界的试验要求参与者在复杂的城市场景中导航,识别和避免障碍物.

主要成果:

在实验室环境中,LBE方法显示障碍物检测率 (63.6%) 与L2 (48.5%) 相比显著更高 (p<0.001).
在现实场景中,物体识别准确度高于LBE (50.2%) 与L2 (41.7%),风险差异为8个百分点 (p=0.081).

结论:

LBE视觉处理算法为视网膜假体用户提供了更好的性能.
增强的VP算法提供的基于深度的视觉线索可以显著改善现实世界的导航结果.
进一步开发先进的VP方法有望恢复功能视力和移动性.

关键词:

生物眼生物眼生物眼避免障碍避免障碍避免障碍障碍物检测障碍物检测障碍物检测障碍物检测视网膜假体是一种视网膜假体.这是一种超体性体.

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