基于破裂检测扩展伪标签的等级存在先验
Nan Wang1, Jie Fang2, Jianfu Yin3
1School of Information Science and Technology, Hainan Normal University, Haikou 571158, China.
The Review of scientific instruments
|December 26, 2024
概括
本研究介绍了一种改进的道路裂检测方法,使用分层先前和扩展的伪标签. 该方法通过逐步完善裂识别来提高准确性,在基准数据集上表现优于现有技术.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 土木工程 土木工程是指土木工程.
背景情况:
- 图像处理对于道路裂检测至关重要,但受到复杂的背景和不平衡数据的挑战.
- 由于这些固有的困难,现有的方法往往难以实现高性能.
研究的目的:
- 开发一个强大而准确的道路裂检测框架.
- 为了解决复杂的背景干扰和道路图像中的类别不平衡的局限性.
主要方法:
- 基于扩展的伪标签提出了一个新的等级存在先验.
- 该框架使用了三种U-Net变体,这些变体按层次训练,伪标签扩展率下降.
- 使用混合损失函数 (IoU,SSIM,焦点损失) 和动态超参数调整.
主要成果:
- 该方法获得了11.36% (CrackTree200),29.76% (Crack Forest) 和26.73% (ALE) 的优异Fβ得分.
- 层次方法逐渐从粗到细的细化检测结果.
- 混合损失和动态调整增强了模型的稳定性和结构表示性.
结论:
- 拟议的方法在道路裂检测方面显示出显著的有效性和优越性.
- 层次化的先前和扩展的伪标签策略为提高裂纹检测准确度提供了一个有希望的方向.
- 该技术显示出在道路维护和检查方面实际应用的潜力.
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