  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.
