The ever-growing number of cameras in every portable device and the evolution of machine learning algorithms have made the advances in computer vision (CV) unavoidable. The best algorithms have achieved an accuracy of up to 96%, meaning they make mistakes only four times out of a hundred. While an average user might rely on computer vision for handwriting recognition or auto-focusing selfies, the technology has a huge potential for business, security, healthcare, and even more. Today, we’ll go over the four use cases crucial for 2020.
Image Recognition Applications in Business Are Endless
The unexpected global crisis and lockdown forced businesses to adapt to remote operations. Every process went online — accepting and processing orders, meetings, and planning sessions. Those who had little to no digitization advances suffered the most. They had to set up websites, social media accounts, and digital workspaces under duress.
Computer vision comes in a variety of forms and solutions for business. For instance, Uploadcare’s image CDN can convert images into the web-appropriate formats, adjust the quality, and make them responsive on a screen of any size. Its feature of recognizing the objects in photos and tagging them can save uncountable work hours and free up employees to perform other critical tasks instead of this mindless routine job. It can be an invaluable time-saver across industries relying heavily upon visual content, including media, e-commerce, e-learning, and others. In business, automation of routine processes is key to increased efficiency, lower expenses, and growing profits.
Healthcare Is Among the First Computer Vision Adopters
Considering the toll that the global spread of COVID-19 has had on the worldwide economy, governments and private companies are now looking for ways to diagnose infectious diseases on the fly. Computer vision comes in handy when coupled with thermal imaging in high-traffic areas, such as airports. It helps security officers screen passengers for high temperature before administering tests.
When used along with the city camera systems, computer vision technology can help prevent further spread of viruses. Smart algorithms analyze the distance between pedestrians and issue warnings to people who do not follow the recommended social distancing protocols. The same principle can be applied to stores and public transportation to ensure optimal occupancy in the time of the pandemic.
However, that is not the only way computer vision affects the healthcare industry. The technology is also used to admit new patients, analyze lab results, and provide acute or elective procedures. For instance, computer vision algorithms can analyze the use of suction canisters and surgical sponges to evaluate the amount of blood loss during childbirth. As a result, doctors can better assess the patient’s condition and administer transfusion as necessary, eliminating guesswork. Fast CT, MRI, or X-ray result analysis is another example of using machine learning in healthcare. Algorithms can locate irregularities faster than human doctors, which results in accelerated diagnosing and start of treatment.
Computer Vision is Vital for Autonomous Driving
While mechanical components, navigation, and user interface are essential for the ongoing operation of a self-driving vehicle, computer vision is at the heart of the concept. Despite the recent advances and partial adoption of autonomous vehicles, a few lethal accidents have pushed the widespread adoption years, if not decades, back. Computer vision algorithms are still not perfect enough for open roads, even if they are getting better every day.
Some of the pressing concerns for CV-powered self-driving cars include:
- Weather conditions affecting object recognition. Heavy snow or rain might render the smartest machine learning algorithms useless. While this may not be a problem for a stationary vehicle, it could cause accidents if precipitation starts when the car is in motion.
- Unexpected obstructions on the road confusing the algorithm. If a wild animal gets on the road, computer vision might not recognize it and attempt to drive over or through the obstruction, resulting in an accident.
- Abrupt switches from autonomous to human driving. Engineers have yet to find a better solution to a problem than returning the decision-making power to the human who might not be ready to take action in a split second in order to prevent an accident or save a life.
The more advanced the computer vision algorithms become, the better they can handle driving conditions. Still, it could take years to create a database that is extensive enough for autonomous cars to recognize any situation and make safe decisions based on the input that confuses existing algorithms.
Digital Maps Bring the World Together
While some densely populated cities are well-documented and easy to navigate, many areas around the world still lack addresses, let alone road names or digital maps for navigation. Unlike the first travelers of the past, we can now rely on satellite imagery and computer vision algorithms to create high-resolution digital maps at any scale.
Hundreds of satellites can provide photos, but they are not usable in most cases, as high-resolution images are not accessible. Even if they were, they would not be an acceptable substitute for a map. Instead of relying on humans to trace the outlines of city blocks and roads, computer vision algorithms can take over this mundane task and perform it quickly and efficiently. The resultant maps are invaluable for the local populace and businesses, as well as government authorities, real estate developers, non-profit organizations, and emergency services.
At the same time, computer vision analysis of satellite imagery can be used by scientists researching species population or migration. Illegal hunting or fishing is equally easy to spot with the help of smart algorithms coupled with detailed, up-to-date photos.
Object recognition for CDN implementation, fast diagnostics in healthcare, smarter self-driving algorithms, and detailed digital maps are just a few ways computer vision changes the world we live in today. There is no denying that this technology will soon become an integral part of our everyday lives and business processes. Now is the time to embrace image processing algorithms and make the most of the new opportunities they provide.