基于定向照明的深度学习模型用于裂和分裂分类
Sanjeetha Pennada1, Jack McAlorum1, Marcus Perry1
1Department of Civil & Environmental Engineering, University of Strathclyde, Glasgow G1 1XJ, UK.
Journal of imaging
|September 26, 2025
概括
本研究介绍了用于混凝土裂检测的定向照明技术,其性能优于传统方法. 多通道神经网络在低光条件下在分类混凝土缺陷方面取得了卓越的准确性.
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:
- 混凝土结构的自主检查需要有效的外部照明,特别是在低光条件下.
- 均扩散的照明在检测混凝土中复杂的裂纹模式方面存在局限性.
- 现有的方法在具有挑战性的照明下,难以准确地对混凝土缺陷进行分类.
研究的目的:
- 提出使用定向照明的新型算法,以改进混凝土缺陷分类.
- 为了解决裂检测中均扩散照明的局限性.
- 提高自主混凝土检查的准确性和可靠性.
主要方法:
- 开发了两个算法:一个合并的神经网络和一个多通道的神经网络.
- 融合神经网络利用最大强度的像素级图像融合从定向图像.
- 多通道神经网络创建一个五通道图像,代表不同的照明方向 (右,下,左,上,扩散).
主要成果:
- 多通道神经网络模型表现出卓越的性能.
- 实现了高评估指标:96.6%的准确性,96.3%的精度,97%的回忆力,96.6%的F1分数.
- 在没有增加评估时间的情况下,超过了FusedNet和其他现有文献模型.
结论:
- 定向照明显著提高了混凝土裂分类的准确性.
- 多通道神经网络是低光环境中自主混凝土检查的一个有希望的方法.
- 未来的工作将探索将这些技术扩展到白盒方法.
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