Industrial Scene Change Detection using Deep Convolutional Neural Networks
This addresses the need for reliable change detection in industrial settings to monitor harmful object additions or removals, though it appears incremental as it builds on existing deep learning approaches.
The paper tackles the problem of detecting and localizing object-level changes between two images of the same industrial scene at different times, addressing challenges like lighting variations and shadows, and reports that the proposed method is more efficient than other methods for real industrial environments.
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.