As deep learning continues to dominate all state-of-the-art computer vision tasks, it is increasingly becoming the essential building blocks for robotic perception. As a result, the research questions concerning the safety and reliability of learning-based perception are gaining increased importance. Although there is an established field that studies safety certification and convergence guarantee of complex software systems for decision-making during design-time , the uncertainty in run-time conditions and the unknown future deployment environments of autonomous systems as well as the complexity of learning-based perception systems make the generalisation of the verification results from design-time  to run-time problematic. More attention is starting to shift towards run-time monitoring of performance and reliability of perception systems with several trends emerging in the literature in the face of such a challenge . This paper attempts to identify these trends and summarise the various approaches on the topic.
As deep learning continues to dominate all state-of-the-art computer vision tasks, it is increasingly becoming an essential building block for robotic perception. This raises important questions concerning the safety and reliability of learning-based perception systems. There is an established field that studies safety certification and convergence guarantees of complex software systems at design-time . However, the unknown future deployment environments of an autonomous system and the complexity of learning-based perception make the generalization of design-time verification to run-time problematic. In the face of this challenge, more attention is starting to focus on run-time monitoring of performance and reliability of perception systems with several trends emerging in the literature . This paper attempts to identify these trends and summarise the various approaches to the topic.
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