  Finding the conceptual difference between the two images in an industrial
environment has been especially important for HSE purposes and there is still
no reliable and conformable method to find the major differences to alert the
related controllers. Due to the abundance and variety of objects in different
environments, the use of supervised learning methods in this field is facing a
major problem. Due to the sharp and even slight change in lighting conditions
in the two scenes, it is not possible to naively subtract the two images in
order to find these differences. The goal of this paper is to find and localize
the conceptual differences of two frames of one scene but in two different
times and classify the differences to addition, reduction and change in the
field. In this paper, we demonstrate a comprehensive solution for this
application by presenting the deep learning method and using transfer learning
and structural modification of the error function, as well as a process for
adding and synthesizing data. An appropriate data set was provided and labeled,
and the model results were evaluated on this data set and the possibility of
using it in real and industrial applications was explained.
