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Updated: Jan 10, 2026

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通过使用深度学习和tunicate swarm算法进行高效的对象检测,为视觉障碍者提供智能辅助系统.

Abdullah M Alashjaee1, Asma A Alhashmi1, Abdulbasit A Darem2,3

  • 1Department of Computer Science, College of Science, Northern Border University, Arar, Saudi Arabia.

Scientific reports
|November 27, 2025
PubMed
概括
此摘要是机器生成的。

一个新的视觉障碍者智能辅助系统 (SASVCP-ODTSA) 使用对象检测来改善日常任务的完成. 该系统达到99.58%的准确性,显著帮助视力受损者.

关键词:
在CapsNet中,我们可以使用CapsNet.对象检测检测对象检测对象检测智能辅助系统是一个智能辅助系统.服装群群算法 服装群群算法视觉障碍的人 视觉障碍的人

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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:

  • 视力障碍在执行日常任务时带来了重大挑战.
  • 准确的物体检测对于开发有效的辅助技术对于视力受损者至关重要.
  • 目前的方法需要改进,以便在不同的环境中可靠地识别对象.

研究的目的:

  • 为视觉障碍者提供智能辅助系统,通过使用衣群算法 (SASVCP-ODTSA) 的物体检测.
  • 提高对象检测准确度和分类性能,以协助视力受损人士.
  • 为了自动检测和分类图像中的对象,以帮助日常活动.

主要方法:

  • 使用中位过 (MF) 进行图像预处理以减少噪声.
  • 使用YOLOV8方法检测物体.
  • 使用CapsNet模型进行特征提取.
  • 使用深度信念网络 (DBN) 进行对象检测和分类,并通过Tunicate Swarm算法 (TSA) 进行优化.

主要成果:

  • 在室内物体检测数据集上,SASVCP-ODTSA模型实现了99.58%的卓越精度.
  • 衣游队算法有效地优化了深信网络的参数,以改善分类.
  • 拟议的系统展示了强大的物体检测和分类能力.

结论:

  • 该SASVCP-ODTSA系统为视力受损者提供了显著的辅助技术进步.
  • 高精度的物体检测和分类对于提高视觉障碍者的独立性和任务完成至关重要.
  • 整合MF,YOLOV8,CapsNet和TSA优化的DBN为对象检测应用提供了一个强大的方法.