基于深度学习的对象检测和周围环境描述,用于视障人士
Raihan Bin Islam1, Samiha Akhter1, Faria Iqbal1
1Electrical and Computer Engineering, North South University, Bangladesh.
Heliyon
|July 24, 2023
概括
本研究介绍了一种低成本的辅助系统,使用深度学习来检测障碍物和描述环境,以帮助视力受损者. 该系统增强了流动性和意识,为传统援助提供了更有效的替代方案.
科学领域:
- 计算机视觉和机器学习
- 为视力受损者提供辅助技术
背景情况:
- 对象检测对于识别和定位视觉数据中的对象至关重要.
- 现有的视力障碍者辅助设备在提供全面的环境意识方面存在局限性.
研究的目的:
- 为视力受损者开发一种低成本,基于深度学习的辅助系统,用于障碍物检测和环境描绘.
- 通过实时音频反增强视力障碍者的独立性和安全性.
主要方法:
- 使用TensorFlow对象检测API,并使用预训练的SSDLite MobileNetV2模型.
- 使用渐变粒子群集优化 (PSO) 优化了MobileNetV2模型.
- 集成的树Pi 4B,摄像头和音频模块 (谷歌文字转语音,PyAudio,播放声,语音识别) 进行实时处理和反.
主要成果:
- 开发了一种实时物体检测系统,能够识别90种不同的物体.
- 使用转移学习创建了一个"环境模式"来描述周围的环境 (天气条件).
- 在桌面和嵌入式Raspberry Pi系统上评估系统性能,测量精度,率和其他关键指标.
结论:
- 拟议的低成本系统有效地帮助视障人士检测障碍物并了解周围环境.
- 这种辅助技术比传统方法 (如白棍) 显著改进.
- 该系统有可能大大改善视力受损用户的日常生活和独立性.
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