An Image-Based Approach for Structural Damage Recognition and Segmentation Using Deep Transfer Learning

Abstract

This research aims to determine the feasibility of using image-based deep learning techniques to inspect the damage and recognize its category in the building components. This analysis helps to determine the structure's health and its quantification in terms of damage by using image segmentation. The validation of the proposed approach is done by using PEER Hub ImageNet (Φ-Net), which is a benchmark dataset of structural images. Miniaturized VGG-16 CNN network and its customized version-based architectures have been tested on the dataset to find their adaptability to structural domain classification. To avoid overfitting in the classes with lesser samples, the transfer learning is applied using a feature extractor and fine-tuning strategies. Different experiments are designed to find the optimal model parameters and their scope for a particular image recognition task. To quantify the damage in recognition tasks such as images with cracks or spalling, pixel-based segmentation is implemented to highlight the regions where the damage occurred and its area in the region of interest. The accuracy scores of 97% for a binary class problem reveal the potential use of transfer learning-based deep learning models in structural damage recognition and segmentation even for a multiclass challenging scene.