PC-CS-YOLO:用于视觉障碍者安全的高精度障碍探测
Jincheng Li1, Menglin Zheng1, Danyang Dong1
1School of Artificial Intelligence and Computer Science, Nantong University, Nantong 226019, China.
Sensors (Basel, Switzerland)
|January 25, 2025
概括
这项研究引入了一种改进的PC-CS-YOLO模型,用于实时盲目障碍物检测. 该系统通过在复杂的环境中提供更快,更准确的避障决策来提高视力障碍者的安全性.
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:
- 避免障碍物和安全对于视力受损的人来说至关重要.
- 复杂的街道环境对当前的盲目障碍物检测系统构成重大挑战.
- 现有的解决方案往往缺乏实时准确性,以有效避免障碍.
研究的目的:
- 开发一个先进的盲目障碍物检测系统.
- 为了提高视力受损者避免障碍物的实时性能和准确性.
- 解决复杂的城市环境中当前系统的局限性.
主要方法:
- 提出了一种使用PC-CS-YOLO模型的新型盲目障碍物检测系统.
- 通过部分卷积前网络 (PCFN) 增强了骨干网络,以最大限度地降低计算负载.
- 引入了跨度注意力融合 (CSAF) 机制,用于在各感官领域强大的多度特征集成.
主要成果:
- 与最先进的网络相比,实现了2.0%的精度,3.9%的回忆和1.5%的mAP50改进.
- 显示了GPU推断速度为20.6ms,比YOLO11.11快15.3ms.
- 该系统满足实时要求,可有效避开盲目障碍.
结论:
- 拟议的基于PC-CS-YOLO的系统显著提高了盲目障碍物检测能力.
- 集成PCFN和CSAF有助于提高准确性和效率.
- 该系统为实时,可靠的避开障碍提供了一个有希望的解决方案,提高了视力受损人员的安全性.
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