Have you ever wondered how a camera detects objects?
For instance, during the pandemic, the government in many parts of the world installed cameras at the airport that can detect people without a face mask and alarm the airport authority. Similarly, phone cameras can detect a face, puppy, and other objects and tell you what the object is. How does a camera do that? Or for that matter, how does your phone camera unlocks your phone only on seeing your face and not others.
A simple answer is artificial Intelligence (AI). A more thoughtful answer is image annotation.
Let me explain.
The first and foremost step in the development of AI models through machine learning ( ML) is obtaining a relevant training set. This training set helps algorithms understand the task at hand, see objects, and even predict outcomes in real life, making various tasks autonomous.
Visual perception –based AI models require images that contain objects that we see in real life. For the model to recognize objects in the images, the images have to be annotated.
Image annotation is the process of creating annotated images for AI models. Image annotation has substantial application in machine learning and artificial intelligence in terms of model success.
The basics of image annotation
The purpose of image annotation is to help machines detect and recognize objects. To do so, images are annotated with metadata for the description of the object. A huge amount of similar data (images) is feed into the model so that it becomes trained enough to recognize the objects when the model encounters a similar product in real-life situations.