  Environmental and surface texture-induced temperature variation across the
bridge deck is a major source of errors in delamination detection through
thermography. This type of external noise poses a significant challenge for
conventional quantitative methods such as global thresholding and k-means
clustering. An iterative top-down approach is proposed for delamination
segmentation based on grayscale morphological reconstruction. A weight-decay
function was used to regularize the reconstruction for regional maxima
extraction. The mean and coefficient of variation of temperature gradient
estimated from delamination boundaries were used for discrimination. The
proposed approach was tested on a lab experiment and an in-service bridge deck.
The result showed the ability of the framework to handle the non-uniform
background situation which often occurred in practice and thus eliminates the
need for inferencing the background required by existing methods. The gradient
statistics of the delamination boundary in the thermal image were indicated as
the valid criterion refine the segmentation under the proposed framework. Thus,
improved performance was achieved compared to conventional methods. The
parameter selection and the limitation of this approach were also discussed.
