  Autonomous vehicles and robots require increasingly more robustness and
reliability to meet the demands of modern tasks. These requirements specially
apply to cameras onboard such vehicles because they are the predominant sensors
to acquire information about the environment and support actions. Cameras must
maintain proper functionality and take automatic countermeasures if necessary.
Existing solutions are typically tailored to specific problems or detached from
the downstream computer vision tasks of the machines, which, however, determine
the requirements on the quality of the produced camera images. We propose a
generic and task-oriented self-health-maintenance framework for cameras based
on data- and physically-grounded models. To this end, we determine two
reliable, real-time capable estimators for typical image effects of a camera in
poor condition (blur, noise phenomena and most common combinations) by
evaluating traditional and customized machine learning-based approaches in
extensive experiments. Furthermore, we implement the framework on a real-world
ground vehicle and demonstrate how a camera can adjust its parameters to
counter an identified poor condition to achieve optimal application capability
based on experimental (non-linear and non-monotonic) input-output performance
curves. Object detection is chosen as target application, and the image effects
motion blur and sensor noise as conditioning examples. Our framework not only
provides a practical ready-to-use solution to monitor and maintain the health
of cameras, but can also serve as a basis for extensions to tackle more
sophisticated problems that combine additional data sources (e.g., sensor or
environment parameters) empirically in order to attain fully reliable and
robust machines. Code:
https://github.com/MaikWischow/Camera-Condition-Monitoring
