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.

本文提出了一种基于深度学习和迁移学习的方法，用于发现工业环境中两幅图像间的概念差异，解决了因环境中物体种类丰富多样以及光照条件改变而导致监督学习方法无法准确区分的问题，并使用添加和合成数据的方法来生成数据集，并对模型结果进行了评估，该方法具有较好的工业应用前景。