论文标题
使用深度学习来定位两个场景的概念差异
Localizing the conceptual difference of two scenes using deep learning for house keeping usages
论文作者
论文摘要
在工业环境中找到两个图像之间的概念差异对HSE目的尤为重要，并且仍然没有可靠且符合的方法来找到主要的差异来提醒相关控制器。由于不同环境中的丰富性和多种物体，在该领域中使用监督的学习方法正面临着一个主要问题。由于两个场景的照明条件发生了急剧变化，因此无法天真地减去这两个图像以找到这些差异。本文的目的是查找和本地化一个场景的两个帧的概念差异，但在两个不同的时间中，并将差异分类为添加，还原和变化。在本文中，我们通过介绍深度学习方法并使用转移学习和误差函数的结构修改以及添加和综合数据的过程来证明该应用程序的全面解决方案。提供了适当的数据集并标记了标签，并在此数据集上评估了模型结果，并解释了在实际和工业应用中使用它的可能性。
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.
