Applications of computer vision are growing, as there are:
- Daily opportunities to generate more data from cameras, sensors, and scanners
- Technological advancements in image-capturing hardware and software
- Increases in computer processing speeds
- Growing annotation tool options available to annotate, or label, data
- Better techniques and algorithms for computer vision, such as convolutional neural networks (CNNs or ConvNets)
- More available, affordable imaging devices (e.g., smartphones, sensors), data storage (e.g., cloud, container), and data annotation tools (e.g., open source software)
Here are examples of common applications of computer vision from select industries:
Technology holds the promise of solving many of the challenges and inefficiencies in the global production and distribution of food. The digital transformation is predicted to impact every stage of the value chain for agriculture, an industry that is centuries old. The digitization of the food and agriculture sectors can strengthen economic, nutritional, and environmental outcomes around the world.
Agriculture technology, also known as AgTech or farmtech, is a growing discipline that applies technology to increase the profitability, efficiency, and sustainability of farms and farming practices. It applies computer vision and other machine learning techniques to replace decisions that farmers traditionally have made on instinct or experience with predictive models that create a more controlled and accurate farming environment.
Farmers use GPS (Global Positioning Systems), IoT (Internet of things) devices, sensors, drones, and autonomous vehicles to capture visual data about everything from growing and harvesting to transport and distribution. Much of the visual data these systems analyze is unstructured and can be annotated to train and deploy a computer vision system.
One AgTech application is precision farming, where technology is used to improve production and reduce waste. For example, computer vision models can learn from annotated images to automate stand counts, predict crop yields, and analyze plant health to determine optimal levels and precise areas to apply fertilizer, herbicides, and seeding. Hummingbird Technologies uses drone and satellite imagery to create computer vision systems that help farmers increase crop yields and farm more sustainably.
Other computer vision applications in AgTech include the optimization of farm staffing by predicting the best time to harvest. It can even power the robotic harvesting technology to do the work. Pasture management is another. Rezatec, a geospatial AI company, uses satellite data in computer vision for pasture management, to optimize the grazing performance of sheep, cattle, and other livestock.
In healthcare, the provision of care is costly, and the quality of care a patient receives can mean the difference between life or death. Since medical imagery became available at the start of the 20th century with the discovery of the X-ray, most visual medical data had to be analyzed by a person with medical expertise.
Today, a large portion of medical visual data comes from imaging technology, such as CT (Computed Tomography) or MRI (Magnetic Resonance Imaging) scanning systems. In an industry where this kind of data is ever-growing, computer vision systems offer promise for analyzing medical images quickly to support healthcare professionals in making faster and more accurate decisions.
Computer vision can analyze large amounts of patient data to detect anomalies and patterns faster than people can. For example, it can be used to identify cancerous tumors from CT scan images and diagnose lung cancer more successfully than radiologists.
It also can be used to power medical AI systems that can alert healthcare practitioners about patient risks and help them diagnose conditions earlier. For example, medical AI is being used to enhance medical professionals’ understanding of health issues and optimize preventive care. It’s being used to annotate X-ray datasets to aid in COVID-19 research.
Security is among the most common applications of computer vision. Security can be applied across many contexts, including device protection, theft identification in retail environments, and violence detection in crowded public spaces, among many others. We use computer vision to safeguard our smartphones and tablets that are equipped with facial recognition to unlock them for our use. In China, some retailers use facial-recognition payment technology, so consumers don’t have to use cash or payment cards.
Computer vision can be used to scan live or recorded video footage to provide security officers with vital information, such as detecting guns in a public area. Facial recognition for this use case - to identify harmful or criminal activity - has come under scrutiny for its use by law enforcement to identify suspects of crimes. The algorithms that underpin this technology have flagged innocent people as suspects of crimes, when algorithms falsely matched their photos with security video footage.
For this reason, several large technology companies announced they have stopped offering, developing, or researching facial analysis software. In general, the tech and security industries have much to learn about how to avoid algorithmic bias and false positives, particularly in cases where data is used to make decisions about individuals’ future freedoms.
For centuries, transportation has applied technology to deliver people, goods, and services to places quickly and efficiently. Today, the industry is in the midst of a wide-ranging digital transformation, driven in part by computer vision. Applying AI is challenging for organizations at every stage of growth, from startup to enterprise, typically due to the shortage of skills to design, annotate data for, and validate new AI-driven ways to solve age-old, painful problems.
We’re seeing growth in computer vision for transportation with the proliferation of autonomous vehicle capabilities, such as intelligent automobile features like emergency braking and lane detection, Tesla’s hands-free driver assist, and autonomous robots for contactless delivery of food and medicine.
The potential benefits of computer vision in transportation are compelling and virtually endless:
- Safer vehicles with fewer accidents and reduced congestion on streets and highways
- Reduced pollution from gridlocked roadways
- Autonomous delivery of food and medicine to at-risk populations in developing nations
- More efficient and predictable public transportation, including railways, subways, and buses
- Greater safety in the transportation of flammable materials via pipelines
Transportation is among the most promising and visible applications of computer vision. However, the promise of autonomous vehicles has not been realized as fast as some had hoped, due to the intensive data preparation and software and technology development requirements to ensure safety. We can expect to see computer vision contribute to significant advancements in transportation for many years to come.