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

arXiv: https://arxiv.org/abs/2208.04884 · HTML: https://mlsanity.com/paper/2208.04884/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 12 references that are in the map.
- First posted on arXiv 9 Aug 2022.

## Builds on (most cited first, 6 of 6)

- [Image Segmentation Using Deep Learning: A Survey](https://mlsanity.com/paper/2001.05566/) (2020)
- [Multi-scale self-guided attention for medical image segmentation](https://mlsanity.com/paper/1906.02849/) (2019)
- [TernausNet: U-Net with VGG11 Encoder Pre-Trained on ImageNet for Image Segmentation](https://mlsanity.com/paper/1801.05746/) (2018)
- [Post-DAE: Anatomically Plausible Segmentation via Post-Processing with Denoising Autoencoders](https://mlsanity.com/paper/2006.13791/) (2020)
- [A Regularized Convolutional Neural Network for Semantic Image Segmentation](https://mlsanity.com/paper/1907.05287/) (2019)
- [Abnormal Event Detection in Urban Surveillance Videos Using GAN and Transfer Learning](https://mlsanity.com/paper/2011.09619/) (2020)

## Built on by (most cited first, 2 of 2)

- [Industrial Scene Change Detection using Deep Convolutional Neural Networks](https://mlsanity.com/paper/2212.14278/) (2022)
- [Attention-Based Spatio-Temporal Approach for Fire and Smoke Detection](https://mlsanity.com/paper/2610.20141/) (2026)

## Abstract (arXiv)

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.