Image recognition has various uses, such as medical imaging, security surveillance, defect detection, etc. But what exactly is it and how it works? In simple terms, it’s the process of identifying an object or a feature in a video or image. Basically, it’s what we do every day with our eyes, but in this case done by AI and Machine Learning.

Why is Image Recognition important?

One of the most important aspects of Image Recognition would be the fact that it allows for tasks to be completed faster and more accurately than if done manually. Image Recognition is a key technique in various applications, such as:

• Automated Driving – Analysing traffic signs, pedestrians and other vehicles is extremely important for automated vehicles, to ensure maximum safety.
• Image Classification – Categorizing images based on their content.
• Visual Inspection – Identifying defective and non-defective parts during manufacturing.

With the use of Image Recognition, all these applications can be optimized. The system identifies everything and uses that information to make decisions that impact the whole system.

Is Image Recognition the same as Object Detection?

While both work in a similar way, they are not the same, but are often used together to provide better results. For example, Image Recognition identifies an object in an image, while Object Recognition identifies instances of those objects in images. You can read more about Object Detection here!

How does Image Recognition work?

There are different techniques for image Recognition, such as Machine Learning and Deep Learning. Depending on the application, either technique might be more suited for you.

Image Recognition through Machine Learning

Using Machine Learning for Image Recognition involves identifying key features of images and using that data as input to train the ML model. Its workflow is the following:

• Training data – The starting point is a collection of images, compiled into their specific categories.
• Feature extraction – The relevant features in each image are selected.
• ML model creation – Those features are then added to a Machine Learning model, which separates them into their respective categories. This information is then used when analyzing new objects.

Image Recognition through Deep Learning

A Deep learning approach to Image Recognition can be used to automatically learn about the relevant features from sample images, automatically identifying them in new images. Its workflow is the following:

• Training data – Just like in the Machine Learning path, the first step is to have a collection of images, compiled into their specific categories.
• DL model creation – You can either create a model from scratch, or better yet, start with a pretrained model to be used as a starting point.
• Training – The chosen model is then trained in your specific needs. The model learns from the data, learning the most important features that are relevant.
• Testing – The model is then tested on data that it hasn’t seen before, to see what it believes the images to be. If the results aren’t satisfactory, the previous steps are repeated until your goal is achieved.

Plate.Vision: License Plate Recognition (LPR System) by MakeWise

The PLATE.VISION – ANPR (Automatic Number Plate Recognition), developed by MakeWise, is a real-time vehicle identification software, used in critical scenarios such as: in the supervision of refueling at fuel filling stations; or in car parking lots, in the authorization, monitoring and control of vehicle access. Here is how Plate.Vision works in authorization, monitoring and control of vehicle access in car parks:

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