一种基于深度学习的流域特征融合方法,用于复杂背景中的道裂细分
Haozheng Wang1,2, Qiang Wang1,2,3, Weikang Zhang1,2
1Zhejiang Scientific Research Institute of Transport, Hangzhou 311305, China.
Materials (Basel, Switzerland)
|January 11, 2025
概括
本研究引入了一种用于高速公路道裂检测的自动化方法,提高了准确性和效率. 优化的深度学习模型显著增强了结构缺陷分析,解决了手动检查的局限性.
科学领域:
- 土木工程 土木工程是指土木工程.
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 高速公路道的结构缺陷,特别是裂,每年都在增加,压倒了传统的手工检查方法.
- 道网络的快速扩张需要先进的自动化检查解决方案.
- 现有的机器视觉和深度学习方法在道裂检测方面面临挑战,原因是复杂的背景和数据标签.
研究的目的:
- 开发高速公路道中裂纹样本集的自动标签和优化算法.
- 提高基于深度学习的裂细分网络的性能.
- 为在道中识别结构缺陷提供高效准确的解决方案.
主要方法:
- 利用了裂功能和分水算法,以实现高效的自动细分,最小的人力投入.
- 通过分析各种网络深度和残余结构配置,优化深度学习破解细分网络.
- 集成的轴提取和流域填充算法来完善细分结果.
主要成果:
- 在不同的外表面条件和干扰因素下,实现了98.78%的裂细分精度.
- 对于裂纹细分,获得了72.41%的欧盟交叉点 (IoU).
- 证明了在具有复杂背景的道中裂纹细分的强大解决方案.
结论:
- 提出的自动标记和深度学习优化算法有效地解决了道裂检测的局限性.
- 增强的细分精度和IOU为基础设施监控提供了可靠的工具.
- 这种方法为土木工程应用中的智能裂细分提供了重大进展.
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