论文标题
工业场景使用深度卷积神经网络的变化检测
Industrial Scene Change Detection using Deep Convolutional Neural Networks
论文作者
论文摘要
就特殊护理应用中不同时间属于同一场景的两个图像中的对象而在两个场景中查找和本地化的概念变化具有很大意义。这主要是由于以下事实：在某些环境中增加或去除重要对象可能是有害的。结果，需要设计一个程序，可以使用机器视觉来定位这些差异。这个问题最重要的挑战是照明条件的变化和场景中的阴影存在。因此，所提出的方法必须对这些挑战有抵抗力。在本文中，引入了基于深层卷积神经网络的方法，使用转移学习，该方法通过智能数据综合过程进行了培训。该方法的结果进行了测试并显示在为此目的提供的数据集上。结果表明，提出的方法比其他方法更有效，可以在各种实际的工业环境中使用。
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.
