# Industrial Scene Change Detection using Deep Convolutional Neural Networks

arXiv: https://arxiv.org/abs/2212.14278 · HTML: https://mlsanity.com/paper/2212.14278/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 37 references that are in the map.
- First posted on arXiv 29 Dec 2022.

## Builds on (most cited first, 22 of 22)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [Dual Attention Network for Scene Segmentation](https://mlsanity.com/paper/1809.02983/) (2018)
- [TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems](https://mlsanity.com/paper/1603.04467/) (2016)
- [Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations](https://mlsanity.com/paper/1707.03237/) (2017)
- [Remote Sensing Image Change Detection with Transformers](https://mlsanity.com/paper/2103.00208/) (2021)
- [A Transformer-Based Siamese Network for Change Detection](https://mlsanity.com/paper/2201.01293/) (2022)
- [Tversky loss function for image segmentation using 3D fully convolutional deep networks](https://mlsanity.com/paper/1706.05721/) (2017)
- [A survey of loss functions for semantic segmentation](https://mlsanity.com/paper/2006.14822/) (2020)
- [A Review on Deep Learning Techniques Applied to Semantic Segmentation](https://mlsanity.com/paper/1704.06857/) (2017)
- [Changer: Feature Interaction is What You Need for Change Detection](https://mlsanity.com/paper/2209.08290/) (2022)
- [Dynamic Weighted Learning for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2103.13814/) (2021)
- [Deep-Anomaly: Fully Convolutional Neural Network for Fast Anomaly Detection in Crowded Scenes](https://mlsanity.com/paper/1609.00866/) (2016)
- [Domain-adversarial neural networks to address the appearance variability of histopathology images](https://mlsanity.com/paper/1707.06183/) (2017)
- [DR-TANet: Dynamic Receptive Temporal Attention Network for Street Scene Change Detection](https://mlsanity.com/paper/2103.00879/) (2021)
- [Hierarchical Paired Channel Fusion Network for Street Scene Change Detection](https://mlsanity.com/paper/2010.09925/) (2020)
- [Learning to Measure Change: Fully Convolutional Siamese Metric Networks for Scene Change Detection](https://mlsanity.com/paper/1810.09111/) (2018)
- [The Time-SIFT method : detecting 3-D changes from archival photogrammetric analysis with almost exclusively image information](https://mlsanity.com/paper/1807.09700/) (2018)
- [Abnormal Event Detection in Urban Surveillance Videos Using GAN and Transfer Learning](https://mlsanity.com/paper/2011.09619/) (2020)
- [Localizing the conceptual difference of two scenes using deep learning for house keeping usages](https://mlsanity.com/paper/2208.04884/) (2022)

## Built on by (most cited first, 1 of 1)

- [Attention-Based Spatio-Temporal Approach for Fire and Smoke Detection](https://mlsanity.com/paper/2610.20141/) (2026)

## Abstract (arXiv)

Finding and localizing the conceptual changes in two scenes in terms of the presence or removal of objects in two images belonging to the same scene at different times in special care applications is of great significance. This is mainly due to the fact that addition or removal of important objects for some environments can be harmful. As a result, there is a need to design a program that locates these differences using machine vision. The most important challenge of this problem is the change in lighting conditions and the presence of shadows in the scene. Therefore, the proposed methods must be resistant to these challenges. In this article, a method based on deep convolutional neural networks using transfer learning is introduced, which is trained with an intelligent data synthesis process. The results of this method are tested and presented on the dataset provided for this purpose. It is shown that the presented method is more efficient than other methods and can be used in a variety of real industrial environments.