Localizing the conceptual difference of two scenes using deep learning for house keeping usages

TL;DR

This paper presents a deep learning approach to localize and classify conceptual differences between two images of the same scene taken at different times, addressing challenges like lighting variations and object diversity in industrial environments.

Contribution

It introduces a novel deep learning method with transfer learning, structural error function modifications, and data synthesis for effective difference localization and classification.

Findings

Achieved accurate localization of scene differences

Successfully classified differences into addition, reduction, and change

Demonstrated applicability in real industrial settings

Abstract

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.