How Computer Vision is transforming different industries
Discover how Computer Vision is transforming transportation and logistics, health, manufacturing, and security
Those of us who can unlock their smartphone with their face, or who like to take a photo of a plant and learn immediately what it is, or have their body measured by an app, are perhaps already familiar with Computer Vision. In a nutshell, Computer Vision (or Machine Vision, or AI vision) is a field of Artificial Intelligence (AI) that trains computers to understand digital images and videos. Computer vision (CV) works similarly to the human vision system, meaning it can “see”, understand what is being seen, and extract complex information from videos and images. More specifically, CV understands videos and images in a similar way as how you would approach solving a jigsaw puzzle. It can identify different shapes and objects on an image/video and piece all the parts together, much like you would do with a puzzle.
Let’s take a look at how Computer Vision is transforming different industries, name some CV applications, and take a glimpse of what we can expect in the future.
How Computer Vision is disrupting different industries
In recent years, we have seen some exciting developments and innovative solutions that are all possible because of CV. For example, using computer vision to help detect and classify skin cancer in patients, automatically analyze the traffic to improve safety, monitor agricultural fields from satellite images to optimize crops, etc.
The number of computer vision applications has been growing because of the recent advances in deep learning (a subset of machine learning based on artificial neural networks), the improvements in computing performance, and because of the growing amount of visual data we collect and generate today, that is used to train and make CV better.
Many industries have been benefiting from the advancements in CV. In particular: transportation and logistics, manufacturing, health, and security are the industries that are impacted the most. Let’s take a look at each of them in more detail below.
Computer Vision in the transportation and logistics industry
Computer vision is helping us see things in new ways, and automate visual controls and tracking in supply chains and logistics, in a cheaper way than other existing technologies. According to DHL, AI vision (or computer vision) is helping them identify damage, classify the damage type, and determine the appropriate corrective action for their transports, faster than ever before. In addition, IBM Watson is using computer vision to improve maintenance, and identify what damaged train wagons look like, and when they need a repair.
Computer vision can also be used to improve demand forecasting, route optimization, warehouse management, and more.
It is projected that AI will help the transportation and logistics industry grow its market size to $10.30 billion by 2030.
Computer Vision in manufacturing
Computer vision can help improve time-efficiency, accuracy, and reduce the cost in manufacturing. Particularly, computer vision can be used for anomaly detection, packaging and safety inspection, vision-guided robots, labeling and tracking of products, and many more. For example, Kawasaki Heavy Industries is using computer vision to accurately assemble each component of their hydraulic pumps used in heavy machinery. Mining companies use computer vision to closely monitor drilling equipment and identify defects and/or damages.
The global value of AI in manufacturing is projected to reach $16.7 billion by 2026.
Computer Vision in Health
AI in healthcare has been a ‘hot’ topic for years, with special attention to computer vision applications. This is no surprise, having in mind that the health industry is overflown with biomedical imaging data like MRI scans, CT scans, ultrasound images, blood smear images, and many, many more. Computer vision can be applied in various areas like radiology, hematology, ophthalmology, cardiology, etc. Here the benefits of using computer vision are not only cost-saving but also life-saving. For example, The Orlando Health Winnie Palmer Hospital for Women and Babies is using computer vision to measure blood loss during childbirth. DeepMind is working together with Moorfields Eye Hospital to detect diabetic retinopathy and age-related macular degeneration, etc.
Artificial Intelligence and healthcare is an exciting and promising fusion. The market is projected to grow from $4.4 billion in 2020, to $45.2 billion by 2026.
Discover more on how computer vision is transforming medical imaging services in one of our previous articles: The use of AI in biomedical imaging.
Computer Vision in security
Computer Vision can play a significant role in a wide range of security applications. From port and railways security to facility security, military surveillance, and fraud detection - the potential applications are numerous. For example, computer vision is used for facial authentication - to help confirm a person’s identity and/or provide better and safer signature services, account accesses, and more. It can also help detect whether workers are wearing protective clothing, help retailers spot fishy behavior or unusual activities at their stores and/or facilities, etc. And with more and more researchers in this field, we are expecting to see far more accurate and reliable AI applications in this industry in the future. There are a number of legal and ethical challenges in that field, though, as illustrated by the current debates on banning facial recognition.
AI in security market was valued at $5.08 billion in 2019, and is expected to reach $14.18 by 2025.
The future of Computer Vision: right in front of us
Automation, better visual tracking and control, better diagnostics...Computer Vision is already empowering a lot of industries. In the future, here is what we can expect.
- Towards more generalization
Research in the field of computer vision is ushering to better algorithms that can automate tasks that require visual cognition. More and more, CV algorithms are able to broaden the type of tasks they can perform, and improve their generalization of visual representations.
- Superhuman capabilities
Thanks to deep learning and artificial neural networks, computer vision is becoming more and more capable of replicating human vision, even surpassing it at some tasks. One example includes a research where an AI model could detect neurological illnesses by looking at CT scans images, faster than radiologists.
With similarly astonishing results in different industries and use cases, computer vision is expected to become increasingly common and promising.