Deep Learning applied to Computer Vision

With the latest advances in Industry 4.0, both Computer Vision and Deep Learning have gained great importance by allowing the simplification and automation of various industrial processes.
For a long time, attempts have been made to use Artificial Intelligence to solve applications that, with conventional vision, required complex algorithms. However, it has not been until the last decade, with the rise of Deep Learning and Convolutional Neural Networks, when it has been possible to obtain promising and efficient results.

Computer Vision

Computer vision consists of extracting data from certain images. These data can be used in an industrial process to, for example, measure objects, read texts or determine if the photographed pieces are valid or, on the contrary, have a defect.
Traditionally, artificial vision has been carried out through the use of operations and algorithms on the images themselves to extract the necessary information. The problem with this type of conventional vision is that it is very inflexible. It requires a lot of work to work with the most extreme cases and it will not provide adequate results with new data that were not contemplated.

Deep Learning

Artificial Intelligence

seeks to provide machines with the ability to learn to obtain the desired information based on training with already known data. Using this technique, it is possible to obtain a much more flexible system that can adapt and obtain very good results when they encounter new situations. In particular, the system that has provided the best results is Deep Learning.
Deep Learning is a type of Artificial Intelligence algorithm that emulates the neural networks of the human brain. Offering results based on the data entered in the network. Due to its great capacity for abstraction, Deep Learning is the type of Artificial Intelligence that is commonly used for Machine Vision.

Convolutional Neural Networks

Convolutional Neural Networks or CNNs are a type of neural network that tries to emulate the behavior of the human visual system. For this reason, these networks are the ones that are usually used with images for the development of Artificial Vision projects that require a type of intelligence, such as object recognition.
Its operation basically consists of applying filters to small areas of the image. Its objective is to find basic patterns such as lines or edges, the deeper you go into the network, the more complex the shapes found. Once all the filters have been applied, the result is entered into a conventional neural network that will provide the result for which said network has been trained.
Its use did not become popular until they began to be used with graphics cards. Being optimized precisely to work with images unlike ordinary processors, it allowed an exponential improvement in training times.

Conventional computer vision or Deep Learning

In the most complex projects, it is usual to apply a combination of both, using conventional Computer vision to preprocess the image and highlight the characteristics that are to be treated. Once the image is preprocessed, it is entered into the Deep Learning system to obtain the final results.
However, with other simpler problems it may not be necessary to apply such a complex process. Although the versatility and good results of Deep Learning have been clearly demonstrated, the use of one system or another largely depends on the type of application to be developed. Since, although it is simpler, the conventional view can provide more reliable results depending on the circumstances, the important thing is to know how to differentiate when this case is met or, on the contrary, it is better to develop a Deep Learning system.
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