CrackNet-Weather:在恶劣天气条件下有效的路面裂检测方法
Wei Wang1, Xiaoru Yu1, Bin Jing1
1College of Computer Science and Technology, Changchun University, No. 6543, Satellite Road, Changchun 130022, China.
Sensors (Basel, Switzerland)
|September 13, 2025
概括
在恶劣天气下准确地检测路面裂,现在可以使用CrackNet-Weather. 这种新方法通过在雨雪中可靠地识别裂,提高了道路安全和维护.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 道路基础设施监测 道路基础设施监测
背景情况:
- 路面裂检测对于道路安全和维护至关重要.
- 恶劣的天气条件 (雨,雪) 显著阻碍了由于可见性问题和复杂的裂纹模式而导致的精确裂纹检测.
研究的目的:
- 在恶劣天气条件下开发一种强大而高效的路面裂检测方法.
- 提高自动化道路检查系统的准确性和实用性.
主要方法:
- 提出了CrackNet-Weather,这是一个新的深度学习模型.
- 集成的头发波形下采样块 (HWDB),条形聚合瓶块 (SPBB) 和动态采样上采样块 (DSUB).
- 在具有雨雪条件的具有挑战性的数据集上进行验证.
主要成果:
- CrackNet-Weather显著超过了现有的基线模型.
- 在平均平均精度方面取得了显著的改进,特别是在具有挑战性的裂纹类型 (低对比度,细微,不规则) 中.
- 证明了检测准确性和计算效率之间的实际平衡.
结论:
- CrackNet-Weather有效地解决了在恶劣天气中检测路面裂的挑战.
- 该方法适用于实际道路检查和大规模部署.
- 提高道路安全和路面维护能力.
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