Session starts at 00 Local time / 00 UTC

Comparison of Detectability Index and Contrast Detection Probability

Watch On Demand

This session is only available to Conference ticket holders.

Register for On Demand

Buy a full conference pass and access ALL content from Detroit and Brussels editions of AutoSens (over 150 sessions!), you can upgrade to a full conference pass here for just £359

Logged in but can't see the session?

Click below to upgrade to a full conference pass and access all presentations from Detroit and Brussels editions of AutoSens, plus full community networking features, private video meeting room, make new connections via search and filter in our community of over 1000 engineers and scientists, and much more…

AVs 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. Of critical importance is an ability to estimate the capability of a camera system to detect objects at distance across a wide array of illumination conditions. Apart from obvious safety considerations, how else will be it possible to ascertain the performance that a DNN should be capable of delivering if the basic objective performance of the input imaging device cannot be determined?

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 defence 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 and conclusion drawn regarding the performance of both metrics.

Speaker

Robin Jenkin

Principal Image Quality Engineer, NVIDIA

Not Enrolled

Comments