  Finding and localizing the conceptual changes in two scenes in terms of the
presence or removal of objects in two images belonging to the same scene at
different times in special care applications is of great significance. This is
mainly due to the fact that addition or removal of important objects for some
environments can be harmful. As a result, there is a need to design a program
that locates these differences using machine vision. The most important
challenge of this problem is the change in lighting conditions and the presence
of shadows in the scene. Therefore, the proposed methods must be resistant to
these challenges. In this article, a method based on deep convolutional neural
networks using transfer learning is introduced, which is trained with an
intelligent data synthesis process. The results of this method are tested and
presented on the dataset provided for this purpose. It is shown that the
presented method is more efficient than other methods and can be used in a
variety of real industrial environments.
