Abstract
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.
Abstract (translated)
我们引入贝叶斯缺陷检测器，以便于粗糙纹理表面上的运动模糊图像的缺陷检测。为了提高贝叶斯检测去除非缺陷像素的准确性，我们开发了一类反射的非局部先验分布，它是通过使用分布模式来减去其密度来构造的。反射的非局部先验迫使贝叶斯检测器在非缺陷位置处接近0。我们进行了实验研究，以证明贝叶斯检测器在消除非缺陷点方面的卓越性能。我们在运动模糊无人机图像中实现贝叶斯检测器，其中探测器成功识别粗糙表面上的冰雹损坏并大大提高整个缺陷检测管道的准确性。
URL
https://arxiv.org/abs/1809.01000
