Tech Briefs Magazine - May 2024 - Sensor-6

Artificial Intelligence
pixel in the image a value representing
the distance from the camera to the
object imaged by that pixel.
5. Convert the values determined by
the range CNN into distances in real
units, typically meters.
6. Combine the information from the
two CNNs to add a third dimension
(depth) to the bounding boxes and
assign a distance to the nearest face of
the bounding box.
7. Use color to code the depth map for
the entire image to produce an informative
picture for display.
8. Assemble the type, location, size, and
range of all objects of interest in the
2D Box X, Y, H, w
with identification
Recognition
CNN
Disparity to Depth
Conversion
Disparity
Lens distortion
correction
Ranging CNN
Fixed Software module
Trained Software module
Figure 3. An inference pipeline. A mix of CNNs and conventional computing provides the data.
(Image: Owl AI)
Depth
Data fusion and
colorization
image for reporting to the equipment
that will take appropriate action.
Figure 3 shows a block diagram of a
sample implementation of this sequence.
This entire set of processes combining
CNN and conventional computation
is called an inference pipeline
(IP). Partitioning the functions
between the CNN and conventional
sections is directed toward maximizing
accuracy and reliability of the results.
Since the Thermal Ranger system is
intended for deployment on real vehicles
in real situations, the entire IP
is capable of field updating whenever
better training data and conversion
algorithms become available.
The Owl Thermal Ranger CNN
in Action
Three steps in the classification and
ranging process are illustrated in Figure
4. The original monocular thermal
image provided to the IP is shown at the
lower right. In the center is a bird's eye
view of the point clouds representing
target objects extracted from the image.
This data can be supplied to an automatic
braking system when a hazard is
detected. At the upper left is the driver
view representation of the output image
showing the recognized objects with
bounding boxes and range labels set in
their natural surroundings.
Figure 4. Classification and labeling. The computer isolates the objects for control and display.
(Image: Owl AI)
Figure 5 is an expansion of the driver
view image from the upper left of Figure
4. In this view, the pedestrian thermal
images can be seen with their associated
2D bounding boxes and range labels.
Notice, on the left, the distinction in
range between two pedestrians walking
together. In this particular example, the
software labels pedestrian ranges up to
50 meters with specific values while pedestrians
outside that range are labeled
as pedestrians (ped) but do not have
range labels. Note that the automobile
headlights in the distance are not found
to be pedestrians and that other automobiles
are detected and marked but
not ranged.
Using thermal images, CNNs can provide
critical information to automatic braking
systems and to drivers both day and
night to help reduce pedestrian accidents.
This
Figure 5. Labeled pedestrians and detected cars. Information can be prepared for driver use. (Image:
Owl AI)
6
www.techbriefs.com
article was written
by Wade
Appelman, Chief Business Officer at Owl
Autonomous Imaging. For more information
go to https://www.owlai.us/.
Sensor Technology, May 2024

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Tech Briefs Magazine - May 2024

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