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 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 box annotation 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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