Bounding box annotation is the
technique of labeling the image data to make it recognizable to machines
through computer vision. Actually, using this technique, a very useful amount
of data sets can be created to train the self-driving cars or autonomous
vehicles.
Bounding Box Annotation for Self-driving Cars
This annotation method can
provide the training data sets to train the AI model for autonomous vehicles
that need to detect and recognize the different types of objects comes while
moving on the city roads or highways. It can annotate the object of interest
recognizable to visual perception based AI models that like self-driving cars
to avoid collision and move safely.
Actually, there could be
different types of data sets can be crated with bounding box annotation to train
the machine learning algorithms detect the wide variety of objects with
accuracy. And below you can find the various types of objects that can be annotated
with bounding box technique.
Bounding Box Annotated Moving Objects
This can be done for video annotation,
in which bounding box annotation are used to capture the object and make it
recognizable to computer vision algorithm. Using the special tools and
software, object of interest is annotated frame-by-frame to create a useful
machine learning datasets for self-driving cars.
Bounding Box Annotated Pedestrians on Road
Humans on road, also need to be
make recognizable to self-driving cars, so that it can take quick action to
avoid accidents and move safely. Bounding Box annotation, can annotate the humans
with full-body annotated images making it recognizable with acceptable level of
accuracy.
Bounding Box Annotated Street Sings & Traffic Lights
Similarly, traffic or street
lights, and signs boards are also important for self-driving cars to identify
and move in the right direction at the right time to avoid any wrong direction
movement. Bounding box annotation can annotate such objects with added names or
metadata for easy recognition.
Bounding Box Annotated Cars and Other Vehicles
A self-driving car not also run
on the street, but there would be other cars, and vehicles, that also should be
visible to self-driving cars. Hence, bounding box annotated datasets can be
used to make other vehicles recognizable through machine learning algorithm
training with right accuracy.
Bounding Box Annotated Data Sets of Animals
Just like humans, making the
animals also recognizable is also important for their safety from self-driving
cars. Here, again bounding box annotation can create the data sets of annotated
animals like dogs, cat and other animals can be found on the streets to make
them recognizable.
Bounding Box Annotated Data for Vehicle Recognition
Apart from other vehicles
detection and recognition, bounding boxannotation can be also used to recognize the types of motor vehicles,
their models, brand and number plates. While creating the training data, such
things are annotated carefully for visual perception models like self-driving
cars.
Bounding Box Annotated Data Set to Recognize Human Faces
Though, for human face detection,
landmark annotation
is more precise and accurate, but bounding box annotation can be also used to
just detect the human faces in frames, car driving seats and faces of people
printed on sign board, hoardings and other places to just make them
recognizable.
Bounding box annotation can
provide very useful amount of training data sets for self-driving cars and
autonomous vehicles. But get such high-quality training data you need to hire
an expert that can provide you the annotated data sets with acceptable quality for
right predictions.
Cogito is one of the well-known companies, providing the image annotation services for machine
learning and AI. It can annotate the huge amount of images with bounding box
annotation to create the machine learning training datasets for self-driving
cars and autonomous vehicles with best accuracy.
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