Computer vision is a branch of Artificial Intelligence (AI) that enables computers and systems to derive meaningful information from digital photos, videos, and other visual inputs, then based on that facts, take steps or offer recommendations. If AI allows computers to think, computer vision allows them to see, watch, and comprehend.
The discipline has made considerable strides in recent years, surpassing humans in various tasks linked to object detection and classification, because of breakthroughs in AI and developments in deep learning and neural networks. The quantity of data we create today, which is subsequently utilized to train and improve CV, is one of the driving factors behind the rise of CV.
How Does Computer Vision Work?
One of the biggest question in AI is: How do our brains work, and how can we interpret it as algorithms? In reality, there are very few functioning and complete theories of brain computation and its inner workings. Hence, despite the fact that Neural Networks are meant to "imitate" the way our brains work, no one knows for a fact that it is legit.
The same theory applies for CV. We are uncertain about how the brain and eyes interpret pictures. Thus, it's impossible to evaluate how closely the algorithms relate to the actual way our brains interpret what our eyes see. So one method of teaching a computer to interpret visual data is to give it photos, thousands, if not millions, of labelled images, and then subject them to various software procedures, or algorithms, that allow the computer to seek find patterns in all the parts that correspond to those labels.
As an example, if a computer is fed with a large amount of fish images, it will subject them all to algorithms that will allow it to assess the colors in the photo, the forms, the distances between the shapes, where things border each other, and so on, in order to identify a profile of what "fish" signifies. When it is completed, the computer should be able to utilize its expertise from being fed previous unlabeled photos to discover the ones that are of fishes.
Prior to the introduction of deep learning, the jobs that CV could accomplish were quite limited and necessitated a great deal of manual coding and work on the part of developers and human operators. However, Machine Learning(ML) offered a novel way to resolving CV issues. Developers no longer required to manually code every single rule into their vision apps thanks to ML.
Deep learning offered a fundamentally new method to ML. Deep learning is based on neural networks, which are general-purpose functions that can solve any issue represented by instances. When you provide a neural network a large number of labelled instances of a given type of data, it will be able to uncover common patterns between those examples and translate them into a mathematical equation that will assist in categorizing future pieces of data.
Deep learning is used in the majority of modern CV applications such as cancer diagnosis, self-driving cars, license plate recognition and face recognition. Deep learning and deep neural networks have progressed from theory to practice as a result of advancements in hardware and cloud computing resources.
Applications of Computer Vision
License Plate Recognition (LPR)
The ability to take photographic footage or photos from license plates and convert the optical data into digital information in real-time is referred to as License Plate Recognition (LPR).
LPR, also known as Automatic Number Plate Recognition (ANPR), is a popular technology for vehicle management operations such as ticketless parking (on and off-street), tolling, stolen vehicle detection, smart invoicing, and many more applications.
How Does LPR Work?
After the camera captures the license plate's picture, the camera firmware converts it into digital (machine-readable) characters using specific Optical Character Recognition (OCR) techniques.
Ever AI Technologies developed our own LPR system as one of our project. Following the detection of the license plate number, essential information such as the car type, manufacturer, production year, and road tax validity may be accessed. In order to develop this system, the model was fed with sufficient images for training purposes. It applies object detection, by which the model is trained with suitable data to achieve its objective. LPR systems can improve security, avoid congestion during peak hours as well as play an important role in the development of smart cities.
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