Specifying and evaluating quality metrics for vision-based perception systems
Robust perception algorithms are a vital ingredient for autonomous systems such as self-
driving vehicles. Checking the correctness of perception algorithms such as those based on
deep convolutional neural networks (CNN) is a formidable challenge problem. In this paper,
we suggest the use of Timed Quality Temporal Logic (TQTL) as a formal language to
express desirable spatio-temporal properties of a perception algorithm processing a video.
While perception algorithms are traditionally tested by comparing their performance to …
driving vehicles. Checking the correctness of perception algorithms such as those based on
deep convolutional neural networks (CNN) is a formidable challenge problem. In this paper,
we suggest the use of Timed Quality Temporal Logic (TQTL) as a formal language to
express desirable spatio-temporal properties of a perception algorithm processing a video.
While perception algorithms are traditionally tested by comparing their performance to …
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