Vision systems, i.e., systems that allow to detect and track objects in images, have gained substantial importance over the past decades. They are used in quality assurance applications, e.g., for finding surface defects in products during manufacturing, surveillance, but also automated driving, requiring reliable behavior. Interestingly, there is only little work on quality assurance and especially testing of vision systems in general. In this paper, we contribute to the area of testing vision software, and present a framework for the automated generation of tests for systems based on vision and image recognition. The framework makes use of existing libraries allowing to modify original images and to obtain similarities between the original and modified images. We show how such a framework can be used for testing a particular industrial application on identifying defects on riblet surfaces and present preliminary results from the image classification domain.
翻译:愿景系统,即能够探测和跟踪图像中物体的系统,在过去几十年中已变得非常重要,用于质量保证应用,例如用于在制造、监视期间发现产品表面缺陷,但也用于自动驾驶,需要可靠的行为。有趣的是,在质量保证方面,特别是一般的愿景系统测试方面,没有做多少工作。在本文件中,我们为视觉软件测试领域作出了贡献,并为基于视觉和图像识别的系统自动生成测试提供了一个框架。框架利用了现有图书馆,得以修改原始图像,并获得原始图像和修改图像之间的相似之处。我们展示了如何利用这一框架测试用于识别利伯特表面缺陷的特定工业应用,并展示图像分类领域的初步结果。