Abstract
Abstract Autonomous vehicles rely on the detection and recognition of objects within images to successfully navigate. Design of camera systems is non-trivial and involves trading system specifications across many parameters to optimize performance, such as f-number, focal length, CFA choice, pixel, and sensor size. As such, tools are needed to evaluate and predict the performance of such cameras for object detection. Contrast Detection Probability (CDP) is a relatively new objective image quality metric proposed to rank the performance of camera systems intended for use in autonomous vehicles. Detectability index is derived from signal detection theory as applied to imaging systems and is used to estimate the ability of a system to statistically distinguish objects, most notably in the medical imaging and defense fields. A brief overview of CDP and detectability index is given after which an imaging model is developed to compare and explore the behavior of each with respect to camera parameters. Behavior is compared to matched filter detection performance. It is shown that, while CDP can yield a first order ranking of camera systems under certain constraints, it fails to track detector performance for negative contrast targets and is relatively insensitive.
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