TFP-YOLO:障碍物和交通标志检测,用于协助视力受损的行人
Zhiwei Zheng1, Jin Cheng1, Fanghua Jin1
1School of Science, Beijing Information Science and Technology University, Beijing 100192, China.
Sensors (Basel, Switzerland)
|September 27, 2025
概括
这项研究介绍了TFP-YOLO,这是一种改进的计算机视觉算法,用于机器导犬. 它增强了对交通标志和障碍物的检测,提高了视力障碍者的安全.
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:
- 视障人士需要先进的移动辅助.
- 机器导犬为传统导犬提供了一个可扩展的替代方案.
- 现有系统需要在复杂的城市环境中改进感知.
研究的目的:
- 为了增强机器导犬的视觉感知.
- 改进交通标志和小障碍物的检测.
- 提高智能移动辅助的安全性和可靠性.
主要方法:
- 开发了TFP-YOLO,这是一个改进的基于YOLOv8的检测算法.
- 集成的三重注意力和三重特征编码 (TFE) 模块.
- 使用P2检测头和WIoU损失功能来提高准确性和稳定性.
主要成果:
- 实现了93.9%的检测准确度和90.2%的精度.
- 模型参数减少了17.2%.
- 显著改善了对交通信号灯,人行道,行人和自行车的检测.
结论:
- 在复杂的场景中,TFP-YOLO增强了机器引导狗的感知.
- 该算法为路径规划和部署提供了强大的技术支持.
- 证明了对视力障碍者移动辅助的重大实用价值.
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