移动-DenseNet:使用基于深度学习的新融合技术检测建筑混凝土表面裂
1Department of Computer Engineering, Faculty of Engineering and Architecture, Erzincan Binali Yıldırım University, 24002, Erzincan, Turkey.
Heliyon
|October 27, 2023
概括
准确检测建筑物中的裂对于安全至关重要. 这项研究引入了一种新的移动密度网人工智能模型,该模型在分类混凝土裂时达到99.87%的准确性,超过了现有方法.
科学领域:
- 土木工程 土木工程是指土木工程.
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 建筑结构完整性依赖于及时检查以检测裂,确保安全和寿命.
- 传统的视觉检查方法是劳动密集型的,容易出现人为错误.
- 人工智能 (AI) 和计算机视觉显示出在结构中自动检测裂的前景.
研究的目的:
- 评估九个已建立的人工智能模型的有效性,用于检测和分类建筑结构中的裂.
- 提出和验证一个新的融合模型,移动密度网,用于增强混凝土裂分类.
- 将拟议模型的性能与传统预训练人工智能模型进行比较.
主要方法:
- 一组具体的表面图像数据集被用于训练和测试人工智能模型.
- 分析了9个预训练的人工智能模型 (Xception,VGG16,ResNet101,InceptionV3,InceptionResNetV2,MobileNetV2,DenseNet169,NASNetMobile,EfficientNetB6),这些模型中的每一个都经过了预训练.
- 通过整合MobileNetV2和DenseNet169架构,开发了一种新的融合模型 - - 移动DenseNet.
主要成果:
- 拟议的移动密度网模型在分类混凝土裂方面取得了惊人的99.87%的成功率.
- 移动-DenseNet模型在与九个传统的预训练模型相比,表现优越.
- 新模型在包括交易数量,密度,功能,复杂性和准确性在内的方面表现出色.
结论:
- 移动密度网络模型为混凝土结构中的自动裂检测提供了一个高度准确和高效的解决方案.
- 人工智能驱动的裂分类显著提高了建筑物的可靠性和安全性评估.
- 这项研究为结构性健康监测应用提供了一个新的,高性能模型.
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