Computer vision has come a long way since its inception in the 1960s when computer scientists first attempted to emulate human sight through computation. In recent years, new deep learning technologies have led to significant advances in computer vision, particularly in image recognition and object detection.
Let’s take a closer look at the evolution of this technology:
1960s: Computer vision research began, but recognizing multiple natural objects with variations in shape was still a challenge.
2014: Deep learning technology and training computers with millions of images from the largest image classification dataset (ImageNet) led to breakthroughs. Deep learning demonstrated superiority over traditional algorithms in tests and challenges.
2016: Deep learning became faster and more efficient with the development of single-stage object detectors, using multilayer convolutional neural networks (CNNs) to simplify feature extraction and description. CNNs became the de facto standard computing framework for computer vision.
2020: Deep learning and edge AI were deployed, enabling computer vision to be realized on low-cost hardware and mobile devices. Numerous deeper and more complex networks were developed for CNNs to achieve near-human accuracy in many computer vision applications.
Computer vision has come a long way, from recognizing common objects to almost human vision. These kind of technologies have evolved significantly with extensive testing, studies, and data.
In our next article, we will showcase six real-life cases of computer vision use in different industries. Don’t miss out!
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