深度神经网络用于基于图像的钢筋混凝土结构视觉数据中的自动损坏分类
1Department of Civil Engineering, Republic of China Military Academy, Kaohsiung, Taiwan.
Heliyon
|October 10, 2024
概括
本研究介绍了一种使用卷积神经网络 (CNN) 和完全卷积网络 (FCN) 的自动化深度学习方法,用于检测和分类智能手机图像造成的钢筋混凝土 (RC) 损伤,从而实现高精度.
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:
- 深度学习的进步提高了图像识别能力.
- 视觉数据分析对于结构性健康监测越来越重要.
- 评估钢筋混凝土 (RC) 损坏传统上需要手动检查.
研究的目的:
- 开发一种用于检测,分类和细分RC结构中的建筑损坏的自动化技术.
- 利用深度学习模型,特别是CNN和FCN,进行基于图像的损害评估.
- 评估智能手机图像对结构损坏分析的有效性.
主要方法:
- 使用了卷积神经网络 (CNN) 和完全卷积网络 (FCN) 的组合.
- 训练模型在2000个数据集上标记了高分辨率的智能手机图像.
- 图像被分为"受损" (混凝土分离,露面钢筋) 和"未受损"区域.
- 精心调整的网络架构和超参数,以实现最佳性能.
主要成果:
- 在对RC结构损坏的分类中获得了98.75%的准确性.
- 在细分受损区域时达到95.98%的准确性.
- 通过CNN和FCN进行有效的特征提取,没有过度装配.
- 验证了深度学习在自动损害评估方面的潜力.
结论:
- 拟议的自动化技术在结构检查中的实际应用方面显示出显著的前景.
- 深度学习模型提供了一种高效且准确的方法来评估来自图像的RC损伤的严重程度.
- 基于智能手机的图像分析与人工智能集成,可以彻底改变结构性健康监测.
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