In the last 20 years, machine vision has undergone a radical transformation from an emerging and experimental technology to an essential tool in multiple industries. From 2005 to the present day, advances in hardware, algorithms and applications have enabled machine vision not only to solve complex problems, but also to drive innovations in areas such as healthcare, industry, agriculture and mobility.
With this article we celebrate 20 years of I-MAS, a company that since 2005 has been leading innovation in engineering and product design. Throughout these two decades, we have worked tirelessly to transform ideas into realities, contributing with innovative solutions in industrial automation, machine vision and deep learning. This anniversary marks not only our journey, but also our continued commitment to excellence and technological change.
Technological limitations and first advances (2005-2010)
In 2005, machine vision was at an early stage of development. Although basic algorithms for edge detection, pattern recognition and image segmentation already existed, their application was limited due to hardware constraints and lack of sufficient data.
Cameras available at the time lacked the resolution and speed needed for complex tasks, and processors did not have the capacity to handle large volumes of data in real time. In addition, the lack of labeled datasets and the limitations of traditional algorithms, such as primitive neural networks, slowed progress.
During this period, advances focused mainly on specific applications, such as quality control on production lines and security systems using basic facial recognition. Despite these developments, the potential of machine vision had not yet been fully exploited.
The Deep Learning Revolution (2010-2015)
The turning point came with the rise of deep learning. In 2012, the AlexNet model, developed by Geoffrey Hinton and his team, marked a turning point by winning the ImageNet competition, demonstrating the superiority of convolutional neural networks (CNNs) in image classification tasks.
The availability of more powerful hardware, especially GPUs (graphics processing units), made it possible to train neural networks with unprecedented efficiency. In addition, the creation of massive datasets such as ImageNet, with millions of labeled images, provided the fuel needed to train more accurate models.
During this period, more sophisticated commercial applications began to emerge. For example, facial recognition systems advanced significantly and began to be used in security, while in the medical field, the first image-assisted diagnosis systems were being explored.
Expansion to new horizons (2015-2020)
Between 2015 and 2020, machine vision experienced an explosion of practical applications. With the development of frameworks such as TensorFlow and PyTorch, the community of developers and scientists were able to access more accessible and powerful tools to implement customized solutions.
During this stage, applications such as:
- Autonomous driving: Companies such as Tesla, Waymo and Uber led the way in the use of artificial vision in autonomous vehicles, using neural networks to interpret the environment in real time.
- Retail and logistics: Object recognition has made it possible to automate warehouses and optimize supply chains. An emblematic example is Amazon Go, which uses artificial vision to enable checkout-free shopping.
- Precision agriculture: Drones equipped with advanced cameras began to analyze crops, detect pests and optimize resources such as water and fertilizers.
Facial recognition has become a ubiquitous technology, from unlocking phones to mass surveillance tools, generating both advances and ethical debates.
Recent innovations and the future (2020-2025)
In recent years, artificial vision has reached amazing levels of accuracy and efficiency, thanks to more advanced models such as generative adversarial networks (GANs) and transformers, which have made it possible to tackle even more complex problems.
The impact of machine vision has been especially notable in areas such as:
- Medicine: Medical image analysis systems have reached levels of accuracy similar to or higher than those of human experts in the diagnosis of cancer, cardiovascular diseases and eye pathologies.
- Industry 4.0: In smart factories, machine vision is used for quality inspection, defect detection and real-time process monitoring.
- Augmented and virtual reality: Applications such as the metaverse and filters in social networks rely heavily on this technology to map and analyze the environment.
In addition, the integration of machine vision with IoT (Internet of Things) devices and edge computing systems has made it possible to run models directly on devices, reducing latency and increasing privacy.
In short, we can say that since 2005, machine vision has evolved from an emerging technology to an essential component of our daily lives. Driven by advances in hardware, software and data, this discipline has not only revolutionized industries, but also opened new frontiers in the way humans interact with the digital world. Looking to the future, its potential seems limitless, but it also raises important questions about how to balance innovation with ethical responsibility.
