Bounding boxes are one the most popular image annotation technique used to train the AI-based machine learning models through computer vision. It can be easily drawn and helps to annotate the object of interest in the pictures and make it recognizable for computer vision.
Bounding boxes used for object detection into many fields including self-driving cars, drones, surveillance cameras and autonomous robots and all sorts of systems using the computer vision. It helps to count the number of obstacles of the same class in a crowd.
How Bounding Box Works in Image Annotation?
Bounding box annotation is a kind of rectangle superimposed over an image in which all key features of a particular object is expected to reside. The main purpose of using this annotation technique is reduce the range of search for those object features conserving the resources used in computing but helps to solve the computer vision problems.
Bounding Boxes for Object Localization
Computers can utilize the image classification or image recognition to simply detect the probability of an object in an image. While other hand with bounding boxes machines can visualize the images with objects localization that helps to solve the computer vision problem.
Actually, object localization algorithm produce the coordinates of the location of an object with respect to the image and using the bounding boxes to localize an object in an image is to represent its location helps computer vision problems in AI-based machine learning.
How Bounding Boxes Used as Training Data?
Bounding boxes annotated images are feed into the machine learning algorithms to identify the objects in the images and store them into the machine neural networks. And when huge quantity of such annotated images are used to train an AI model through computer vision the model give the predictions learn from these annotated images.
While developing the AI-based model for self-driving cars, drones, surveillance cameras and autonomous robots, bounding boxes are used to understand the objects by computers through machine vision that further helps machines to detect such objects in the images.
However, there are many other image annotation techniques used to make objects recognizable for the computer vision but it depends on the machine learning model training and AI project that requires certain types of training data for computer vision.
And as much as quality training data will be used to develop such models the predictions will be precise. And bounding boxes is one of the most preferred image annotation methods for object localization that can be used in image and videos having multiple types and class of objects making them recognizable into the crowd solving the computer vision problem.
Anolytics is one of the companies providing the image annotation service for machine learning training data needs. It is also well-versed in bounding boxes image annotation technique for different types of objects in images with accuracy. It can provide the best quality training data sets for autonomous vehicles, ecommeroce and robotics model perceptions making the computer vision more precise and usable in machine learning and AI.
