混凝土裂检测和分离:一个特征融合,裂隔离和可解释的基于人工智能的方法
Reshma Ahmed Swarna1,2, Muhammad Minoar Hossain1,2, Mst Rokeya Khatun2
1Department of Computer Science and Engineering, Mawlana Bhashani Science and Technology University, Tangail 1902, Bangladesh.
Journal of imaging
|September 27, 2024
概括
这项研究介绍了一种用于混凝土裂检测的智能方案,该方案使用来自卷积神经网络和手工制作的方法的融合特征. 先进的方法实现了高准确性,并提供了解释,改善了结构安全评估.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 结构健康监测 结构健康监测
背景情况:
- 目前基于图像的裂检测方法缺乏对各种条件的性能理解.
- 挑战包括图像分辨率,细裂纹检测和区分裂纹类型.
- 需要改进的算法来进行准确的结构评估.
研究的目的:
- 开发一个智能系统来识别裂,并从图像中量化它们的百分比.
- 将深度学习和手工制作的方法的功能融合在一起,以改进裂检测.
- 整合可解释的AI,以提高结构分析的清晰度和信任度.
主要方法:
- 使用ResNet-50 (卷积神经网络) 和曲线变换 (手工制作) 的特征融合.
- 通过线性区分分析 (LDA) 的优化和使用 eXtreme梯度增强 (XGB) 的分类.
- 结合图像值,形态运算和轮检测以量化裂的新型算法;整合LIME和Grad-CAM++以实现可解释性.
主要成果:
- 在两个数据集上实现了99.93%和99.69%的准确性,超过了最先进的方法.
- 开发了一种新的算法,用于隔离和量化裂区域.
- 在裂检测准确性和可解释性方面表现出卓越的性能.
结论:
- 拟议的方法提供了一种可靠的工具,用于实时检测混凝土结构中的裂.
- 通过及时维护,提高结构安全,并通过准确的评估来促进维护.
- 通过透明的AI决策,增加对工程实践的信任和采用.
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