使用混合深度学习模型实时检测和定位混凝土柱中的蜂巢缺陷
Sourav Kumar Das1, Biswarup Yogi2, Raj Majumdar2
1Department of Civil Engineering, Manipal University Jaipur, Jaipur, 303007, Rajasthan, India.
Scientific reports
|July 1, 2025
概括
本研究介绍了一种混合深度学习模型,用于在蜂中检测和细分具体的缺陷. 该模型实现了高精度,为基础设施健康监测提供了强大的解决方案.
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:
- 混凝土结构对于基础设施至关重要.
- 早期检测缺陷对于结构完整性和安全至关重要.
- 现有的缺陷检测方法可能缺乏精度或效率.
研究的目的:
- 开发一种混合深度学习模型,用于准确检测和实例细分具体缺陷.
- 结合YOLOv5和Mask R-CNN的优势,实现更好的缺陷定位.
- 为民用基础设施的结构健康监测提供可靠的工具.
主要方法:
- 开发了一个混合模型,集成YOLOv5用于对象检测和Mask R-CNN用于例如细分.
- 1991年注释图像的数据集被用于培训和评估.
- 该系统包含了增强的预处理,规范化和非最大抑制 (NMS).
主要成果:
- 该模型实现了高训练 (98.26%) 和验证 (97.80%) 的准确性.
- 记录了出色的性能指标,包括0.9210的子相似系数和0.9752.2的平均精度 (mAP).
- 拟议的方法展示了强大的细分性能和优越的概括能力.
结论:
- 混合深度学习模型为具体的缺陷检测和细分提供了高度准确和高效的解决方案.
- 这种方法非常适合在结构缺陷检查中的现实应用.
- 这项技术有助于改善民用基础设施健康监测和维护.
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