基于边界特征的道路标记损坏程度检测 增强和不对称的大视野背景特征
Zheng Wang1, Ryojun Ikeura1, Soichiro Hayakawa1
1Faculty of Engineering, Mie University, Tsu 514-8507, Mie Prefecture, Japan.
Journal of imaging
|August 27, 2025
概括
这项研究引入了一种评估道路标记损坏严重性的新方法. 这种方法可以准确量化损坏程度,提高道路维护规划和效率.
科学领域:
- 运输工程
- 计算机视觉
- 基础设施管理
背景情况:
- 道路标志对于交通安全至关重要,需要有效的维护.
- 目前的方法缺乏准确的道路标记损坏程度量化.
- 评估损坏程度对于优先考虑维护至关重要.
研究的目的:
- 开发一个基于细分的新框架来估计道路标记损坏程度.
- 提高道路标记损坏评估的准确性和效率.
- 支持运输基础设施的自动化维护计划.
主要方法:
- 一个涉及剩余和完整标记区域的双分段战略.
- 纳入一个不对称的大视野上下文 (ALFVC) 模块用于多尺度特征.
- 集成监督的边界特征增强 (BFE) 模块以实现形状和边界精度.
主要成果:
- 实现了89.44%的平均细分精度,超过了5.86%的基线.
- 在专有数据集上,损害量化显示出最低0.22%的错误率.
- 这种方法在实际应用中被证明是有效和轻量级的.
结论:
- 拟议的框架准确地估计了道路标记损坏的严重程度.
- 这种方法提高了道路管理的精度和效率.
- 它为自动化基础设施维护计划提供了宝贵的支持.
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