The AI Revolution: AI Image Recognition & Beyond

Although image recognition and computer/machine vision may appear to be interconnected terms, image recognition is a subset of computer vision. The logistics sector might not be what your mind immediately goes to when computer vision is brought up. But even this once rigid and traditional industry is not immune to digital transformation. Artificial intelligence image recognition is now implemented to automate warehouse operations, secure the premises, assist long-haul truck drivers, and even visually inspect transportation containers for damage. Another crucial factor is that humans are not well-suited to perform extremely repetitive tasks for extended periods of time.

For example, a computer system trained with an algorithm of images of cats would eventually learn to identify pictures of cats by itself. Computer vision is a set of techniques that enable computers to identify important information from images, videos, or other visual inputs and take automated actions based on it. In other words, it’s a process of training computers to “see” and then “act.” Image recognition is a subcategory of computer vision.

What is Image Recognition Software?

There’s also the app, for example, that uses your smartphone camera to determine whether an object is a hotdog or not – it’s called Not Hotdog. It may not seem impressive, after all a small child can tell you whether something is a hotdog or not. But the process of training a neural network to perform image recognition is quite complex, both in the human brain and in computers.

With training datasets, the model could classify pictures with an accuracy of 85% at the time of deploying in production. One of the eCommerce trends in 2021 is a visual search based on deep learning algorithms. Nowadays, customers trendy photos and check where they can purchase them, for instance, Google Lens. Each image is annotated (labeled) with a category it belongs to – a cat or dog.

Image Recognition vs. Computer Vision

With the help of this information, the systems learn to map out a relationship or pattern in the subsequent images supplied to it as a part of the learning process. Surveillance is largely a visual activity—and as such it’s also an area where image recognition solutions may come in handy. OK, now that we know how it works, let’s see some practical applications of image recognition technology across industries.

Now you know how to deal with it, more specifically with its training phase. Medical staff members seem to be appreciating more and more the application of AI in their field. Through X-rays for instance, Image annotations can detect and put bounding boxes around fractures, abnormalities, or even tumors. Thanks to Object Detection, doctors are able to give their patients their diagnostics more rapidly and more accurately. They can check if their treatment is functioning properly or not, and they can even recognize the age of certain bones. Before using your Image Recognition model for good, going through an evaluation and validation process is extremely important.

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