Image recognition is one of the most fashionable technologies in artificial intelligence applications. If image recognition plays this leading role today, it is thanks to the break brought by deep learning and neural networks, but not only. Unlike other branches of artificial intelligence, image recognition existed long before the work done on neural networks in the early 2010s. In fact, there have been proven image recognition techniques since 1960s. The strength of this technology therefore lies in mastering a range of techniques and sensors available to solve a business problem (infrared, ultraviolet, opencv, OCR, 3D).
Image Recognition is an extremely powerful technology when it comes to automatically recognizing an object that humans can recognize with the naked eye. The autonomous car is the best known application, but also one of the complexes to implement due to the multitude of objects to be recognized in real time and the analysis of each of the situations that must be learned to the algorithm. Image Recognition is already used in several industries, such as : - waste treatment: improve sorting - agriculture: detecting diseases or weeds - retail: do your shopping without having to go to the checkout as for AmazonGo - health: analyze x-rays or identify cancer cells - transport: optimizing traffic lights systems to improve traffic flow
The key to achieve an Image Recognition project is a great dataset. The more you are able to provide a large amount of images of what you want to recognize, the more likely you are to see your application performing. However, to achieve an Image Recognition project, a large amount of data is not sufficient. This data must also be qualitative: clear images, taken under the same conditions as those which the algorithm will have to process. To ensure that your dataset meets these conditions, it is best to have it tested with a datascientist. The other essential prerequisite for an Image Recognition project is business expertise. Business knowledge is a key accelerator in the development of the solution. This knowledge can be used to define business rules that will be applied during the algorithm's post-processing. This will make it possible improve the performance of the algorithm.
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