Surveillance and Rescue UAVs

Bring intelligence to the drones

The TULIPP solution aims to fill this processing gap by using its good performance to weight and power consumption to weight figures.
In this UAV use case we plan to use computer vision algorithms such as depth estimation and obstacle detection to evaluate the surroundings and make the UAV more intelligent.

Medical X-Ray Imaging

Reduce radiation dose by 75%

In the Tulipp project our aim is to try and reduce the level of radiation by 75%. As a result of this, more powerful image processing will be required in order to still be able to see many of the small details in the human body that are crucial during surgery. Since most operating rooms are small, the device needs to be small and mobile. A system that integrates the processing close to the sensor would be ideal to help reduce extraneous wires.

Advanced Driver Assistance

Safer driving experience

ADAS vision systems require real time, low latency processing, at high to very high computational load. They need to be robust and reliable, and will often be treated as safety critical systems. The Tulipp project addresses all these questions – by offering a toolset and standardization, it will help the designers to focus on the image processing application rather than platform details.

Project partners

THALES (France) Coordinator
RUHR-UNIVERSITAT BOCHUM (Germany)
SUNDANCE MULTIPROCESSOR TECHNOLOGY LTD (United Kingdom)
HIPPEROS SA (Belgium)
FRAUNHOFER GESELLSCHAFT ZUR FORDERUNG DER ANGEWANDTEN FORSCHUNG EV (Germany)
NORGES TEKNISK-NATURVITENSKAPELIGE UNIVERSITET NTNU (Norway)
Synective Labs AB (Sweden)
EFFICIENT INNOVATION SAS (France)

Project information

START DATE: 09 February 2016

DURATION: 3 years

EC CONTRIBUTION: 3’897’055 euro

COORDINATOR: Dr Philippe MILLET

Objectives

Define a reference platform for low power image processing applications.
Reference platform instantiation through use applications (Medical imaging, Robotics imaging, Automotive).
Demonstrate and plan improvements of defined key performance indicators.
Start-up and manage an ecosystem of stakeholder to extend image processing norms.