Robust Crack Detection in Concrete Structures Using Transfer Learning Techniques

Abstract

Structural health monitoring is needed to gauge the safety and sustainability of civil infrastructure. Conventional crack detection methods are through manual inspection which is time consuming, labor intensive and can easily be compromised through human error. The article offers a powerful method of identifying cracks in concrete buildings through transfer learning with deep convolutional neural networks. Models like ResNet50 and MobileNetV2, which are pretrained, are trained on a dataset of real crack images of concrete to be able to classify between cracked and non-cracked surfaces. On model generalization and performance, data augmentation and preprocessing methods are implemented. The experimental findings indicate that the suggested approach is highly accurate, precise and recalls, despite having a small training set. It is an effective system that may be used in real-time structural health monitoring applications.