SegNet
demonstration, Credit: Alex Kendall
(December 21, 2015) Two
technologies which use deep learning techniques to help machines to see and
recognise their location and surroundings could be used for the development of
driverless cars and autonomous robotics – and can be used on a regular camera
or smartphone.
Two newly-developed systems for driverless cars can identify
a user’s location and orientation in places where GPS does not function, and
identify the various components of a road scene in real time on a regular
camera or smartphone, performing the same job as sensors costing tens of
thousands of pounds.
The separate but complementary systems have been designed by
researchers from the University of Cambridge and demonstrations are freely
available online. Although the systems cannot currently control a driverless
car, the ability to make a machine ‘see’ and accurately identify where it is
and what it’s looking at is a vital part of developing autonomous vehicles and
robotics.
The first system, called SegNet, can take an image of a
street scene it hasn’t seen before and classify it, sorting objects into 12
different categories – such as roads, street signs, pedestrians, buildings and
cyclists – in real time. It can deal with light, shadow and night-time
environments, and currently labels more than 90% of pixels correctly. Previous
systems using expensive laser or radar based sensors have not been able to
reach this level of accuracy while operating in real time.