一个新的卷积神经网络,用于增强路面裂检测的连续性
Jinhe Zhang1,2, Shangyu Sun3,4,5, Weidong Song2
1School of Geomatics, Liaoning Technical University, Fuxin, 123000, China.
Scientific reports
|December 5, 2024
概括
本研究介绍了CPCDNet,这是一个用于连续地板裂提取的新型网络. 该模型显著提高了在复杂的道路环境中裂检测的准确性和连续性.
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:
- 路面上的裂损害了道路结构的稳定性和安全性,需要准确的损害评估.
- 传统的卷积神经网络在精确的路面裂提取方面面临挑战,导致错过或错误的检测.
研究的目的:
- 开发一个网络,CPCDNet,用于连续的路面裂提取.
- 在复杂的环境中提高裂纹检测的连续性和准确性.
主要方法:
- 介绍CPCDNet,一种新的网络架构.
- 整合了裂纹对齐模块 (CAM) 来改善特征对齐.
- 利用加权边缘交叉损失函数 (WECEL) 来优先考虑裂纹边缘.
主要成果:
- CPCDNet获得了高的平均交叉与欧盟 (mIoU) 分数:CFD的77.71%,Crack500的80.36%,Deepcrack537的91.19%和Gaps384的71.16%.
- 与现有网络相比,拟议的方法在路面裂提取方面表现出优越的连续性和准确性.
结论:
- CPCDNet有效地解决了在路面裂检测中传统方法的局限性.
- 该模型的改进导致了更可靠的道路健康评估和改善道路安全.
相关概念视频
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Concrete pavement joints are essential for maintaining the structural integrity and longevity of pavement by controlling where and how the pavement cracks. These joints can be categorized based on their functions, such as contraction or control joints, construction joints, isolation joints, and expansion joints.
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01:20Microcracking in Concrete
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01:28Types of Non-structural Cracks in Concrete
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01:23Abrasion Resistance of Concrete
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