一项关于使用T-Norm,T-Conorm操作员来增强自动驾驶系统夜间图像的直觉模糊生成功能的研究
M S Ragavendirane1, S Dhanasekar2
1Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Chennai, Tamilnadu, 600127, India.
Scientific reports
|September 29, 2025
概括
这项研究使用直觉模糊集 (IFS) 和T-Norm/T-Conorm运算符来增强自动驾驶的夜间图像. 该方法在低光条件下提高图像清晰度和细节,提高车辆的感知.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 图像处理 图像处理
背景情况:
- 自动驾驶系统需要高质量的夜间图像,以确保安全导航.
- 传统方法在低可见度条件下扎,影响决策.
- 直觉模糊集 (IFS) 提供了一种新的方法,通过考虑属于和不属于的程度来表示不确定性.
研究的目的:
- 使用直观模糊逻辑来增强低可见度夜间图像的自动驾驶.
- 评估各种T-Norm和T-Conorm操作员在图像增强方面的有效性.
- 提高自动驾驶汽车在具有挑战性的低光环境中的感知能力.
主要方法:
- 应用直觉的模糊生成器与选定的T-Norm (爱因斯坦) 和T-Conorm (哈马赫,韦伯,W-概率) 运算符相结合.
- 对图像增强的分析,重点是对比度提升,噪声抑制和照明改进.
- 在夜间驾驶数据集上使用标准图像质量指标 (SSIM,PSNR,相关系数) 的验证.
主要成果:
- 与最先进的技术相比,提出的基于IFS的方法显著提高了图像清晰度,并保留了关键的视觉细节.
- 灵敏度分析证实了IFS组件和操作员在不同低光条件下的强度和稳定性.
- 在增强对比度,减少噪音和改善照明方面实现了卓越的性能.
结论:
- 将直观的模糊集与特定的T-Norm和T-Conorm运算集成为夜间图像增强的有效策略.
- 开发的方法在低光条件下增强了自动驾驶车辆的感知,有助于更安全的驾驶.
- 这项研究为自动驾驶技术的关键挑战提供了一个创新的解决方案.
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