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.
翻译:在工业环境中发现两种图像在概念上的差异对于健康、安全及安全目的来说特别重要,仍然没有可靠和可兼容的方法来找到相关控制器的重大差异。由于不同环境中的物体丰富和种类繁多,这一领域的监督学习方法的使用正面临一个重大问题。由于两个场景的照明条件发生急剧甚至轻微的变化,不可能天真地减去两种图像以找到这些差异。本文件的目的是找到一个场景的两个框的概念差异,并将其本地化,但分为两个不同的时间,并将差异分类到外加、减少和改变。在本文件中,我们通过介绍深层学习方法,使用转移学习和错误功能的结构修改,以及添加和合成数据的程序,展示了这一应用的全面解决方案。提供了一套适当的数据集并贴上标签,对数据集的模型结果进行了评估,并解释了在实际和工业应用中使用模型的可能性。