Deep Learning (DL) has revolutionized the capabilities of vision-based systems (VBS) in critical applications such as autonomous driving, robotic surgery, critical infrastructure surveillance, air and maritime traffic control, etc. By analyzing images, voice, videos, or any type of complex signals, DL has considerably increased the situation awareness of these systems. At the same time, while relying more and more on trained DL models, the reliability and robustness of VBS have been challenged and it has become crucial to test thoroughly these models to assess their capabilities and potential errors. To discover faults in DL models, existing software testing methods have been adapted and refined accordingly. In this article, we provide an overview of these software testing methods, namely differential, metamorphic, mutation, and combinatorial testing, as well as adversarial perturbation testing and review some challenges in their deployment for boosting perception systems used in VBS. We also provide a first experimental comparative study on a classical benchmark used in VBS and discuss its results.

翻译：深度学习(DL)使视觉系统(VBS)在诸如自主驾驶、机器人外科手术、关键基础设施监视、空中和海上交通控制等关键应用方面的能力发生革命性的变化。通过分析图像、声音、视频或任何类型的复杂信号,DL大大提高了这些系统的情况意识。与此同时,在越来越多地依赖经过训练的DL模型的同时,VBS的可靠性和可靠性受到挑战,因此,彻底测试这些模型以评估其能力和潜在错误变得至关重要。为了发现DL模型中的缺陷,已经对现有的软件测试方法进行了相应的调整和完善。在本篇文章中,我们概述了这些软件测试方法,即差异、变异、突变和组合测试,以及对抗性扰动测试,并审查了在部署这些测试时在增强VBS中使用的感知系统方面存在的一些挑战。我们还就VBS中使用的典型基准进行了首次实验性比较研究,并讨论了其结果。