  Despite decades of efforts using thermography to detect delamination in
concrete decks, challenges still exist in removing environmental noise from
thermal images. The performance of conventional temperature-contrast approaches
can be significantly limited by environment-induced non-uniform temperature
distribution across imaging spaces. Time-series based methodologies were found
robust to spatial temperature non-uniformity but require the extended period to
collect data. A new empirical image filtering method is introduced in this
paper to enhance the delamination detection using blob detection method that
originated from computer vision. The proposed method employs a Laplacian of
Gaussian filter to achieve multi-scale detection of abnormal thermal patterns
by delaminated areas. Results were compared with the state-of-the-art methods
and benchmarked with time-series methods in the case of handling the
non-uniform heat distribution issue. To further evaluate the performance of the
method numerical simulations using transient heat transfer models were used to
generate the 'theoretical' noise-free thermal images for comparison.
Significant performance improvement was found compared to the conventional
methods in both indoor and outdoor tests. This methodology proved to be capable
to detect multi-size delamination using a single thermal image. It is robust to
the non-uniform temperature distribution. The limitations were discussed to
refine the applicability of the proposed procedure.
