CNN-Based Deep Architecture for Reinforced Concrete Delamination Segmentation Through Thermography
This work addresses bridge health monitoring by improving delamination detection, but it is incremental as it applies existing deep learning methods to a specific domain.
The paper tackled the problem of accurately segmenting delamination shapes in reinforced concrete bridge decks using thermography, achieving satisfactory performance in profiling delamination shapes through a CNN-based framework.
Delamination assessment of the bridge deck plays a vital role for bridge health monitoring. Thermography as one of the nondestructive technologies for delamination detection has the advantage of efficient data acquisition. But there are challenges on the interpretation of data for accurate delamination shape profiling. Due to the environmental variation and the irregular presence of delamination size and depth, conventional processing methods based on temperature contrast fall short in accurate segmentation of delamination. Inspired by the recent development of deep learning architecture for image segmentation, the Convolutional Neural Network (CNN) based framework was investigated for the applicability of delamination segmentation under variations in temperature contrast and shape diffusion. The models were developed based on Dense Convolutional Network (DenseNet) and trained on thermal images collected for mimicked delamination in concrete slabs with different depths under experimental setup. The results suggested satisfactory performance of accurate profiling the delamination shapes.