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Finding and localizing the conceptual changes in two scenes in terms of thepresence or removal of objects in two images belonging to the same scene atdifferent times in special care applications is of great significance. This ismainly due to the fact that addition or removal of important objects for someenvironments can be harmful. As a result, there is a need to design a programthat locates these differences using machine vision. The most importantchallenge of this problem is the change in lighting conditions and the presenceof shadows in the scene. Therefore, the proposed methods must be resistant tothese challenges. In this article, a method based on deep convolutional neuralnetworks using transfer learning is introduced, which is trained with anintelligent data synthesis process. The results of this method are tested andpresented on the dataset provided for this purpose. It is shown that thepresented method is more efficient than other methods and can be used in avariety of real industrial environments.

Industrial Scene Change Detection using Deep Convolutional Neural Networks