Autonomous vehicles and robots require increasingly more robustness and reliability to meet the demands of modern tasks. These requirements specially apply to cameras because they are the predominant sensors to acquire information about the environment and support actions. A camera must maintain proper functionality and take automatic countermeasures if necessary. However, there is little work that examines the practical use of a general condition monitoring approach for cameras and designs countermeasures in the context of an envisaged high-level application. We propose a generic and interpretable 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 (defocus blur, motion blur, different noise phenomena and most common combinations) by comparing traditional and retrained machine learning-based approaches in extensive experiments. Furthermore, we demonstrate how one can adjust the camera parameters (e.g., exposure time and ISO gain) to achieve optimal whole-system capability based on experimental (non-linear and non-monotonic) input-output performance curves, using object detection, motion blur and sensor noise as examples. Our framework not only provides a practical ready-to-use solution to evaluate 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.
翻译:自主车辆和机器人需要越来越强的稳健性和可靠性,以满足现代任务的需求。这些要求特别适用于照相机,因为它们是获取环境信息和支持行动的主要传感器。相机必须保持适当的功能,必要时采取自动对策。然而,几乎没有什么工作来审查对照相机实际使用一般状况监测方法的情况,并在设想的高水平应用范围内设计反措施。我们提议基于数据和物理底部模型的照相机采用通用和可解释的自我健康维护框架。为此,我们确定两个可靠、实时、有能力的估测器,用以测量条件差的照相机的典型图像效果(焦点模糊、运动模糊、不同噪音现象和最常见的组合),办法是在广泛的实验中比较传统的和经过再培训的机器学习方法。此外,我们证明人们如何能够调整摄像参数(例如,暴露时间和ISO的收益),以便根据实验(非线和非运动底部)的参数,实现最佳的全系统性工作表现曲线,利用物体检测、运动模糊性和感官噪音作为例子。我们的框架只能将更多的健康状况和感官噪音作为基础,用来充分评估更多的健康状况。我们的框架只能用来作为一种现实的推介根基。我们的数据基础,而不是用来用来进行更多的推算。