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It is no secret that computer vision is increasingly present in our routine, as already mentioned in articles on the subject in our blog. It is important to emphasize that the great current applicability of computer vision is the result of a favorable technological context. In this text we will address some recent advances that have allowed us to expand the use of computer vision to the level we are currently at. Vision technologies in embedded systems have had a significant proliferation since the early 2000s, allowing the emergence of high-performance and low-cost imaging sensor architectures. Digital cameras have had significant improvements in specifications such as resolution, frame rate and energy efficiency. These improvements allowed an increase in the number of possible applications in the areas of industrial inspection and automation. • Do you want to know everything about Computer Vision? Click here!
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Advances in computer vision
The most significant technological change in image sensors is the transition from CCD to CMOS technology between the 1990s and 2000s. CMOS sensors resulted in improvements mainly in the resolution and frame rate of acquired images, with a reduction in noise level and energy consumption, thus becoming the most common sensors on the market. These technological innovations allowed computer vision to increasingly move from the area of academic research to industrial and commercial use. In the 2000s, mobile phones began to be produced with embedded cameras, and with the popularization of smartphones, computer vision began to be more present for the population. The greater number of sensors and users increased the acquisition of images for databases, and also popularized the direct use of computer vision algorithms through applications. In addition to cameras, other sensors of more specific application (such as industrial cameras, infrared and hyperspectral cameras, radars, orbital sensors, LiDAR sensors, x-ray, ultrasound, and magnetic sensors) also had significant technological advances in recent decades, with their production cost also being reduced and their use being more widespread. In addition to the greater ease of acquisition, there is a growing number of image databases for each sensor, facilitating the development of applications involving computer vision.
Programming Languages
In the field of development, various advancements have occurred in academia in recent years, collaborating for the emergence of artificial intelligence algorithms capable of performing tasks such as object detection and image segmentation more precisely and with lower computational cost. One can mention the development of convolutional neural networks such as Mask R-CNN and YOLO v3. Several programming languages nowadays, such as Python, Julia, R, and Java, make it possible to develop artificial intelligence models, with each language having specific advantages depending on the application. Of these languages, Python can be cited as the most popular language, thanks to its large amount of open-source tools and libraries and its easy interpretation and integration with other languages. Open-source libraries like PyTorch, TensorFlow, Keras, Pandas, and Scikit-Learn have made data manipulation and analysis processes more accessible, as well as the construction of machine learning and deep learning models, multiplying the number of models in development in the field. [caption id=”attachment_2881” align=”alignnone” width=”640”] Programming[/caption]
Data and storage in computer vision
Other major advances in the area involve a significant increase in processing capacity. The great limiting factor for the use of artificial intelligence before the turn of the century was the lack of storage and processing power of computers. Moore's Law estimates that computer memory and speed double every year, and today machines have become powerful enough that these applications are possible. Today we have computers capable of storing large amounts of data, which allows for more accurate applications without the algorithms necessarily having to improve. Deep learning algorithms improve their performance when receiving a larger amount of data, and machines with increasingly fast internet connections allow access to databases across the network. It is also possible to use the network to perform processing tasks remotely, thus not requiring the acquisition of processors with high computational capacity. The offering of cloud computing services such as Microsoft's Azure and Amazon Web Services for applications with high computational demand is increasingly common. However, sometimes applications are required in environments with poorer infrastructure, and an internet connection cannot always be relied upon. Because of this type of limitation, edge computing technologies are also common today. Edge computing technologies refer to machines capable of performing all data processing and analysis tasks at the location where they are collected, which could be, for example, a gas pipeline, industrial machinery, an aircraft engine, or MRI equipment. Edge computing also facilitates applications that require real-time processing and low latency, such as the use of augmented and virtual reality, and can over time reduce the demand on datacenters for the use of cloud computing.
Uses of computer vision and A.I.
Along with edge computing, increasingly powerful and affordable Graphics Processing Units (GPUs) have emerged, enabling diverse computer vision applications. There are specific solutions for computer vision applications, such as embedded development platforms with integrated GPUs (like Nvidia's Jetson series), allowing the creation of integrated computer vision platforms with easy portability. This equipment can be used to integrate computer vision solutions into devices like autonomous vehicles or drones. The use of drones has brought important advantages to tasks such as monitoring large areas, including plantations, construction sites, transmission lines, and highways. Deploying computer vision solutions in these tasks can represent significant gains in efficiency and accuracy in analysis. With the rapid speed at which these technological advancements have been emerging, it is difficult to predict what innovations will arise in the coming years and decades. What can be said is that there is a trend for artificial intelligence to be increasingly present in different parts of our routine. Services like self-driving cars and smart homes have been anticipated for some years, and thanks to artificial intelligence, they are getting closer and closer to becoming a reality. Wherever there is market demand for safer and more efficient solutions, it is a safe bet that artificial intelligence will meet that demand sooner or later.
Fabio Caraça
Fábio Caraça is the Chief Growth Officer at Pix Force. He leads Pix Force's transformation into a scalable SaaS operation, combining strategic vision, culture, and high-impact execution.
