Tech Briefs Magazine - May 2024 - Sensor-5

additional training, the CNN improves
in accuracy and the loss drops. Typically,
the training data set is presented to the
CNN many times, in sessions known as
epochs, to reinforce the selectivity of
the CNN.
However, it is possible that continued
repetition of the training data can cause
the CNN to recognize only those new
images that very closely resemble the training
images. This phenomenon, called
overfitting, results from the CNN being
shown the training images too many times.
Attempting to make the CNN perform
perfectly during training almost always
leads to additional loss with new images.
Care must be taken to stop training when
the CNN operation is near its optimum.
Training the Thermal Ranger System
The Thermal Ranger system needs
two types of scene information to formulate
its report on the scene contents, the
classification of each VRU with its location
in the image and the distance from
the camera to each classified object.
To begin the process, a CNN generates
a map of the entire image, extracting
at each pixel a value called " inverse
depth, " which is inversely proportional
to the distance of the object imaged in
that pixel. To assure that the distances
will be accurately reported in the final
result, the CNN is trained on images
having significant content within the
range of useful distances where the true
distance to this content is known.
Detection
Human
(1.8m x 0.5)
3.5 × 1 pixels / 2.1 ppm
(Something is there)
11 × 3 pixels / 6.3 ppm
(A person is there)
23 × 6 pixels / 12.1 ppm
(The person looks like a civillian)
Recognition
Identification
Vehicle
(2.2m x 2.2m)
2 × 2 pixels / 0.9 ppm
(Something is there)
6 × 6 pixels / 2.7 ppm
(A vehicle is there)
12 × 12 pixels / 5.5 ppm
(The vehicle looks like a minivan)
Figure 1. Typical detection, recognition, and identification requirements. Classification falls between
recognition and identification. (Image: Infiniti Electro-Optics)
Another CNN is trained on images of
the objects to be classified so that bounding
boxes can be placed around them to
assign positions in the picture. Then the
content of the bounding boxes can be
segmented into pixels that cover the object
and pixels that show the background.
The outputs of the two CNNs are
then combined to assign distances to
the pixels representing just the object.
The result is a report on each object
that contains its class, its position in the
image, and its distance from the camera.
What the Thermal Ranger System
Needs to See
It is desirable to have a system capable
of recognizing and locating VRUs in
clear as well as degraded visual environments,
day and night. Recognizing and
locating are two quite different functions
having a certain hierarchy. The
order of events goes something like this:
1. Accept raw data from a camera and
perform any required normalization so
that all objects fall within the analyzer
dynamic range at all image locations.
2. Apply a CNN to the task of recognizing
all objects of interest and identifying
them according to type, this process is
commonly referred to as classification.
3. Using the classification data, assign
the locations for the sides of a two-dimensional
bounding box and a class
to each object.
4. Simultaneously with step 2, apply another
CNN to produce a range map
for the entire image, assigning to every
Figure 2. CNN training images. Objects to be classified are indicated in color. (Image: Owl AI)
Sensor Technology, May 2024
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