  We introduce a Bayesian defect detector to facilitate the defect detection on
the motion blurred images on rough texture surfaces. To enhance the accuracy of
Bayesian detection on removing non-defect pixels, we develop a class of
reflected non-local prior distributions, which is constructed by using the mode
of a distribution to subtract its density. The reflected non-local priors
forces the Bayesian detector to approach 0 at the non-defect locations. We
conduct experiments studies to demonstrate the superior performance of the
Bayesian detector in eliminating the non-defect points. We implement the
Bayesian detector in the motion blurred drone images, in which the detector
successfully identifies the hail damages on the rough surface and substantially
enhances the accuracy of the entire defect detection pipeline.
