May 2, 2023

> **_NOTE:_** This post is part of my [Machine Learning Series](https://eecue.com/blog/machine-learning-series---exploring-the-world-of-ai-ml) where I’m discussing how AI/ML works and how it has evolved over the last few decades. Computer vision, the field of AI that enables computers to interpret and understand visual information from the world, has undergone significant advancements over the past decade. The ability to analyze images and videos, recognize objects, and understand visual scenes has opened up a multitude of applications in fields such as healthcare, autonomous vehicles, and security. In this blog post, we will explore the key milestones and breakthroughs that have shaped the evolution of computer vision over the last ten years. [](https://eecue.com/photo/fd3a0c5490) ## The Rise of Deep Learning in Computer Vision ### ImageNet and the Convolutional Neural Network (CNN) Revolution One of the most transformative moments in computer vision came in 2012 with the [ImageNet Large Scale Visual Recognition Challenge (ILSVRC)](https://image-net.org/challenges/LSVRC/). The competition, which involved classifying images into 1,000 different categories, was won by AlexNet, a deep [convolutional neural network (CNN)](https://en.wikipedia.org/wiki/Convolutional_neural_network) designed by Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton. [AlexNet](https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks) significantly outperformed traditional computer vision algorithms, marking the beginning of the deep learning revolution in computer vision. ### Object Detection and Segmentation Advances Following the success of AlexNet, new architectures and techniques emerged for tasks such as object detection and segmentation. Models like [R-CNN](https://arxiv.org/abs/1311.2524), [YOLO (You Only Look Once)](https://arxiv.org/abs/1506.02640), and [Mask R-CNN](https://arxiv.org/abs/1703.06870) improved the accuracy and speed of object detection and instance segmentation. ## The Expansion of Computer Vision Applications ### Healthcare and Medical Imaging Advancements in computer vision have had a profound impact on healthcare, particularly in medical imaging. Deep learning models can now [detect diseases](https://www.nature.com/articles/s41591-018-0107-6) from medical scans with accuracy comparable to human experts, aiding in early diagnosis and treatment. ### Autonomous Vehicles and Robotics Computer vision has played a crucial role in the development of [autonomous vehicles](https://www.aurora.tech/blog/computer-vision-in-self-driving-cars/), enabling them to perceive their surroundings and make safe driving decisions. Additionally, computer vision is used in robotics for tasks such as navigation, manipulation, and [human-robot interaction](https://arxiv.org/abs/2307.15363). ## The Emergence of Vision Transformers and Self-Supervised Learning