表边界网:一个表边界提取网络,考虑拓规律性
Jing Yang1, Shengqiang Zhou2, Xialing Li1
1School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China.
Sensors (Basel, Switzerland)
|July 12, 2025
概括
TableBorderNet使用一种新的语义细分方法,准确地从退化的工程图纸中提取表框. 这种方法通过提高数据提取精度来增强数字存档和智能基础设施开发.
科学领域:
- 计算机视觉 计算机视觉
- 数字图像处理 数字图像处理
- 机器学习 机器学习
背景情况:
- 从扫描的道路工程图纸中精确地提取表格边界,对于数字化转型和智能基础设施至关重要.
- 退化的边界,模糊和相邻的字符对自动表解析构成重大挑战.
- 现有的方法与视觉扭曲作斗争,需要大量的注释数据,限制了现实世界的适用性.
研究的目的:
- 从复杂和退化的工程图纸中开发一个可靠的框架来精确地提取表边界.
- 为了提高工程档案中自动表格结构划分的准确性和可靠性.
- 减少对手工注释数据集的依赖,以训练深度学习模型.
主要方法:
- 提出了TableBorderNet,这是一个语义细分框架,指导在明确的行和列方向上提取特征.
- 引入了拓意识损失函数,以确保降解输入的结构一致性.
- 实施了一种自主监督的生成策略,以模拟退化模式并减少注释需求.
主要成果:
- 在表边界提取方面实现了94.2%的交叉跨欧盟 (IoU).
- 显示了1.07%的低拓误差,表明结构精度高.
- 在复杂的视觉条件下,在精度和稳定性方面超过了现有的方法.
结论:
- TableBorderNet提供了一个实用且可扩展的解决方案,用于在工程图中精确地提取表边界.
- 该框架大大推动了工程档案的数字化转型,并支持数据驱动的道路资产管理.
- 该方法能够处理退化输入并最大限度地减少注释要求的能力提高了其现实世界的实用性.
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