EU project Tulipp demonstrates low-power vision-based systems: Page 3 of 4

July 11, 2019 //By Julien Happich
Tulipp
Concluding three years of research, the Tulipp (Towards Ubiquitous Low-power Image Processing Platforms) Consortium is announcing its results with the release of a comprehensive reference platform.

Medical X-ray image before (left) and after (right)
processing to remove sensor noise.

Tulipp’s medical X-ray imaging use case demonstrates advanced image enhancement algorithms for X-ray images running at high frame rates. It focuses on improving the performance of X-ray imaging Mobile C-Arms, which provide an internal view of a patient’s body in real-time during the course of an operation, to deliver increases in surgeon efficiency and accuracy with minimal incision sizes, aids faster patient recovery, lowers nosocomial disease risks and reduces by 75% the radiation doses to which patients and staff are exposed.

ADAS adoption is dependent on the implementation of vision systems or on combinations of vision and radar and the algorithms must be capable of integration into a small, energy-efficient Electronic Control Unit (ECU). An ADAS algorithm should be able to process a video image stream with a frame size of 640x480 at a full 30Hz or at least at the half rate.

The Tulipp ADAS use case demonstrates pedestrian recognition in real-time based on Viola & Jones algorithm. Using the Tulipp reference platform, the ADAS Use Case achieves a processing time per frame of 66ms, which means that the algorithm reaches the target of running on every second image when the camera runs at 30Hz.

Tulipp’s UAV use case demonstrates a real-time obstacle avoidance system for UAVs based on a stereo camera setup with cameras orientated in the direction of flight. Even though we talk about autonomous drones, most current systems are still remotely piloted by humans. The use case uses disparity maps, which are computed from the camera images, to locate obstacles in the flight path and to automatically steer the UAV around them. This is the necessary key towards fully autonomous drones.


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