深入探讨脆性:为脆边缘检测提供指导标签精细化
概括
噪音标签在基于学习的边缘检测中会导致厚边缘. 精制人为标记的边缘可以提高边缘的清晰度,提高光流估计和图像分割等任务的性能.
科学领域:
- 计算机视觉 计算机视觉
- 图像处理 图像处理
背景情况:
- 基于学习的边缘检测模型通常会产生厚的边缘.
- 由于标签噪音,现有的方法在与边缘脆度作斗争.
研究的目的:
- 在基于学习的边缘检测中调查厚边缘预测的原因.
- 通过改进培训标签,提出一种提高边缘脆度的方法.
- 为了证明精致标签的有效性,用于训练清晰边缘探测器.
主要方法:
- 开发了一种用于定量评估的新边缘脆度测量方法.
- 为人类标记的边缘提出了Canny引导的精细化技术.
- 通过使用精细的边缘图来训练现有的边缘检测模型.
主要成果:
- 确定杂的人类标签是粗边缘预测的主要原因.
- 精制的边缘图在训练模型中显著改善了边缘清晰度 (17.4%至30.6%).
- 在使用PiDiNet骨干而没有非最大抑制的情况下,在Multicue数据集上实现了最先进的性能,并改进了ODS (12.2%) 和OIS (12.6%).
结论:
- 优先考虑标签质量而不是模型设计对于实现清晰边缘检测至关重要.
- 拟议的Canny-guided边缘改进有效地增强了清晰边缘探测器的训练数据.
- 清晰边缘检测在下游应用中表现出卓越的性能,例如光流估计和图像细分.
相关概念视频
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