在典型的退化条件下,地下管道走廊的现场理解系统
Jing Wang1, Ruiyao Xing1, Meng Zhou1
1School of Electrical and Control Engineering, North China University of Technology, Beijing 100144, China.
Sensors (Basel, Switzerland)
|January 10, 2026
概括
这项研究增强了地下公用事业道检查,改进了对象识别和场景描述. 新方法在低光条件下提高了准确性,确保了更安全的操作.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器人技术 机器人技术 机器人技术
背景情况:
- 地下公用事业道检查需要准确的现场理解,以确保安全操作.
- 道中的低光条件对当前的检查技术构成重大挑战.
研究的目的:
- 用增强的图像语义细分来开发用于低光环境的对象识别方法.
- 微调视觉语言模型以从道图像中生成场景描述.
- 将这些组件集成到实时场景理解中,在公用事业道中.
主要方法:
- 低光图像增强和语义细分用于对象识别.
- 用真实的地下公用事业道图像数据微调视觉语言模型.
- 系统集成用于实时图像处理和场景理解.
主要成果:
- 改进的物体识别模型显示,平均准确度增加了近1%.
- 微调的视觉语言模型在准确性和回忆方面实现了超过70%的改进.
- 集成系统可实现实时场景理解和描述生成.
结论:
- 拟议的方法显著提高了在低光道环境中的对象识别和场景理解.
- 微调视觉语言模型可以提高它们在特定域数据上的性能.
- 综合系统为实时,准确的地下公用事业道检查提供了一个实用的解决方案.
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