Tech Briefs Magazine - May 2024 - Sensor-4

Convolutional
Neural Networks Enable
Nighttime Imaging
Safety sensors on cars don't work well at night.
One solution is long wave thermal imaging.
I
ncreasing regulatory concentration
on improving the protection of vulnerable
road users (VRUs) against
vehicle collisions at night has led
to new evaluations of proven imaging
modalities that might quickly, effectively,
and economically identify VRUs
and measure their positions relative to
moving vehicles.
Using just one thermal infrared camera
and carefully trained convolutional
neural networks (CNNs), the Thermal
Ranger® system from Owl Autonomous
Imaging (Fairport, NY) can locate and
classify VRUs in the dark from their
thermal signatures. Thermal imaging
is
Imaging at Night
However, detection alone is not
enough to support decision-making -
each VRU must be classified by type and
tagged with a distance from the vehicle.
To accomplish both of these functions,
the Owl system uses a complex of CNNs
from a single thermal image to extract
all the information required for automatic
emergency braking decisions.
Detection, Recognition, and
Identification (DRI)
The images supplied to
well-suited for imaging after dark
because it sees objects using their own
emitted infrared energy rather than
relying on illumination provided by
streetlights or headlights. Further, thermal
infrared passes through rain, fog,
and other obscurants relatively unimpeded,
providing useful images even in
degraded conditions.
the CNN
must contain sufficient detail to allow
extraction of the desired information.
The level of information available from
an image depends on how many sample
points (pixels) span the important objects.
Figure 1 shows the three levels that
have been typically assigned - detection,
recognition, and identification - based
on conclusions drawn by a human observer
using a video display. When the
observer is a CNN, a fourth category
becomes most important - classification
- which is between recognition and identification,
corresponding to about 10 pixels
per meter. This resolution is sufficient
to allow the CNN to discriminate between
adults and children and to determine the
pose of the pedestrian.
CNN Fundamentals
Convolutional neural networks are
computer simulations of groups of neurons
that can be trained to indicate
when they see objects that correlate
strongly with marked objects in a set
of training images. Figure 2 shows typical
training images with the objects
to be detected indicated in color. The
coloring is done manually by experienced
technicians to assure that reliable
image data is presented during training.
Providing the CNN with labels to attach
to training objects is called supervision.
When training starts, the CNN knows
nothing, so its accuracy is very low, and
its failure rate (loss) is very high. With
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Sensor Technology, May 2024
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Tech Briefs Magazine - May 2024

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