  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.
