通过室内物体检测基于辅助系统,为视障人士提供智能框架,使用YOLO与循环神经网络的循环神经网络
Marwa Obayya1, Fahd N Al-Wesabi2,3, Wafi Bedewi4
1Department of Biomedical Engineering, College of Engineering, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia.
Scientific reports
|December 12, 2025
概括
这项研究介绍了一种使用深度学习的智能物体检测系统,以帮助视力受损的人. 这种新型模型达到99.74%的准确性,为视力受损者增强了安全导航和对象识别.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 生物医学工程 生物医学工程
背景情况:
- 视力障碍对独立性构成重大挑战,特别是在环境导航和物体识别方面.
- 基于深度学习 (DL) 的实时对象检测 (OD) 为辅助技术提供了潜在的解决方案.
- 现有的方法需要改进,以便在帮助视力受损者 (VIP) 时可靠地在现实世界中应用.
研究的目的:
- 为VIPs提出一个基于智能物体检测的辅助系统,使用常春藤优化算法 (IODAS-IOA).
- 通过精确的物体检测,增强视障人士的独立和安全导航.
- 开发一个有效的OD系统,利用先进的DL技术.
主要方法:
- 使用高斯过 (GF) 进行图像预处理以减少噪声.
- 使用新的YOLOv12方法进行物体检测.
- 使用DenseNet161进行特征提取,并通过具有注意力机制的双向封闭反复单元 (BiGRU-AM) 进行图像分类.
- 使用Ivy优化算法 (IOA) 调整参数以提高分类性能.
主要成果:
- IODAS-IOA模型在对象检测任务中表现出卓越的性能.
- 在室内OD数据集上进行了广泛的实验,验证了该系统的有效性.
- 达到99.74%的高精度,超过了最近的技术.
结论:
- 拟议的IODAS-IOA系统显著提高了视力受损用户对象检测的准确性.
- 这种先进的辅助系统使视力障碍者具有更大的环境意识和独立性.
- 整合DL,新的OD方法和优化算法显示了未来辅助技术的前景.
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