Fan Ching-Lung
Department of Civil Engineering, Republic of China Military Academy, Kaohsiung, Taiwan.
Heliyon. 2024 Sep 19;10(19):e38104. doi: 10.1016/j.heliyon.2024.e38104. eCollection 2024 Oct 15.
Significant strides in deep learning for image recognition have expanded the potential of visual data in assessing damage to reinforced concrete (RC) structures. Our study proposes an automated technique, merging convolutional neural networks (CNNs) and fully convolutional networks (FCNs), to detect, classify, and segment building damage. These deep networks extract RC damage-related features from high-resolution smartphone images (3264 × 2448 pixels), categorized into two groups: damage (exposed reinforcement and spalled concrete) and undamaged area. With a labeled dataset of 2000 images, fine-tuning of network architecture and hyperparameters ensures effective training and testing. Remarkably, we achieve 98.75 % accuracy in damage classification and 95.98 % in segmentation, without overfitting. Both CNNs and FCNs play crucial roles in extracting features, showcasing the adaptability of deep learning. Our promising results validate the potential of these techniques for inspectors, providing an effective means to assess the severity of identified damage in image-based evaluations.
深度学习在图像识别方面取得的重大进展扩大了视觉数据在评估钢筋混凝土（RC）结构损伤中的潜力。我们的研究提出了一种将卷积神经网络（CNN）和全卷积网络（FCN）相结合的自动化技术，用于检测、分类和分割建筑物损伤。这些深度网络从高分辨率智能手机图像（3264×2448像素）中提取与RC损伤相关的特征，分为两组：损伤（暴露的钢筋和剥落的混凝土）和未损伤区域。通过一个包含2000张图像的标记数据集，对网络架构和超参数进行微调可确保有效的训练和测试。值得注意的是，我们在损伤分类中达到了98.75%的准确率，在分割中达到了95.98%的准确率，且没有过拟合。CNN和FCN在特征提取中都发挥着关键作用，展示了深度学习的适应性。我们的 promising 结果验证了这些技术对检查人员的潜力，为基于图像的评估中评估已识别损伤的严重程度提供了一种有效手段。
