Are you one of the people who think that computer vision can be useful in cars (where it helps detect pedestrians and other obstacles), but not necessarily in everyday work? If so, you should read on, because today, we are going to show you some of the most interesting image analysis and computer vision applications in, yes, everyday work in various industries and niches.
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As you know from our other blog posts – computer vision in fintech, computer vision is an AI-related technology that helps applications, algorithms, and software understand and analyze image files or camera images. And the truth is, currently, one of the most common and recognizable applications of computer vision is in the motor industry. Modern cars are filled with assistants that aid the driver. Some of them are based on computer vision. Such an assistant for the driver comes in handy, especially in poor weather conditions. It scans the car’s surroundings and analyzes potential threats, obstacles, and other relevant situations that the driver needs to react to while driving, to name just a pedestrian crossing the road.
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This technology works similarly in other fields and niches. In general, in image analysis and computer vision applications, we have various cameras and sensors and an AI-fueled algorithm that interprets images. The outcome is transferred directly to the user who can take action according to what the computer vision algorithm shows.
This raises the question, is this technology relevant in everyday work? Are there any image analysis and computer vision applications that really help people doing their jobs? The answer is yes, and in fact, this technology becomes more and more popular, even in sectors you’d never expect!
Image analysis and computer vision applications improve workflow
In this article, we are going to show you some of the most exciting image analysis and computer vision applications that remarkably improve workflow and help people save hundreds of hours previously spent on repetitive tasks.
We are going to show you how image analysis and computer vision applications improve workflow in:
- The financial industry
- Retail and e-commerce
- The industrial sectors
Let’s start with the first point on our list:
IMAGE ANALYSIS AND COMPUTER VISION APPLICATIONS: THE FINANCIAL INDUSTRY
Image analysis and computer vision applications in finances is such a broad topic; we decided to write a separate article about them. Here, we want to mention just one crucial application–new user verification. Every banking company has an obligation to verify the identity of every new user. Among many other reasons, it’s all about preventing money laundering. The traditional user verification process (known in the banking sector as KYC) takes at least several hours.
Computer vision can, and actually does, speed things up. One of many companies utilizing computer vision algorithms in order to accelerate the verification process is Revolut. If you want to open a Revolut account, you have to take two pictures–one of your ID and one of your face. Next, Revolut’s computer vision algorithms verify these two images and compare them. If they match, your account is open. No additional human work is required. Ergo, Revolut employees have more time for more complicated tasks and operations.
If you are interested in how image analysis and computer vision applications change the modern financial industry, we advise you to read the article mentioned above. It covers all of the most significant applications in the fintech sector.
IMAGE ANALYSIS AND COMPUTER VISION APPLICATIONS: RETAIL AND E-COMMERCE
Let’s start with e-commerce. Every online store should pay attention to maintaining high-quality photos and descriptions of the products they offer. It’s quite a straightforward process if you have, let’s say, 50 products. But when you have more than 1,000 products, things can get tricky. As an entrepreneur, you have to make sure your offer is up to date and correct. However, monitoring hundreds of products, their descriptions, and photos is a time-consuming task.
If neglected, it can be a source of poor customer experience. Imagine a typical situation, where your customer orders a product that’s listed as “available”, but when it’s time to ship it, it suddenly turns out that you don’t have that product in your inventory. And you have to write an explanatory email and apologize for misleading your customer. No online store owner likes these situations. And this is where computer vision can be used.
Computer vision algorithms help you maintain high-quality, correct descriptions and pictures in your store. You see, image analysis and computer vision applications can regularly scan your virtual warehouse and correct any product attribution mistakes, or add missing visual product attributes, such as color. The algorithm can flag potential errors and mark them as a computer-generated notification so that, if needed, your employees can verify the mistake and update the listing.
Now, let’s switch to more traditional retail.
AMAZON GO STORES
It’s a convenience and grocery store where you can simply walk in, grab what you want, and walk back out. At the heart of this concept is the computer vision-based machine learning algorithm used to track and estimate everyone’s intention in the store. Their computer vision algorithms are capable of detecting:
In short, the Go store “knows” when you walk in, what is in your cart, and when you walk out. Every customer in the store is continuously monitored. Amazon’s algorithms even detect what exactly each person near a shelf is doing with their arms! The deep learning network is used in order to assess who took what.
What about payments? Amazon calls it “Just Walk Out Technology”. Their algorithms detect when products are taken from or returned to the shelves and keeps track of them in a virtual cart. When a customer finishes their shopping, they just leave the store. A receipt is issued and sent to the customer, and money is taken from the user’s Amazon account.
Computer vision really changes retail. Everything goes quicker and seamlessly. Moreover, there is even no need to hire more employees. The real, self-service shops will soon become a standard!
What about safety? The US-based company called StopLift has developed an AI-based computer vision algorithm that reduces theft and other losses at brick-and-mortar stores. Their image analysis and computer vision applications, called ScanItAll™, comprises video analytics technologies applied to inventory shrinkage at the checkout. ScanItAll automatically analyzes video from checkouts every moment to detect inventory shrinkage visually, even when it leaves no data trail, and that’s including sweethearting, scan-avoidance, self-checkout loss, basket-based loss, operational errors, etc.
Healthcare is also a tremendous example of how computer vision can improve workflow. In fact, when it comes to surgery, diagnoses, and medical image analysis, computer vision algorithms can significantly improve the human physicians’ work, or even automate it together. Again, since the subject of computer vision in healthcare is very broad, we have written a separate article about it. It’s linked in this section. If you run a healthcare institution, you should definitely read it!
In this article, we will just mention the use case of image analysis and computer vision applications in healthcare that helps in making accurate diagnoses. In general, computer vision algorithms can detect even the slightest presence of an anomaly that may be missed out by human analysts. That’s because they are trained on massive amounts of data (to make that possible and efficient, computer vision algorithms are typically combined with machine learning algorithms that help them in “interpreting” images). The use of computer vision in healthcare diagnosis can provide high levels of precision. In many instances, higher than human physicians can offer.
LYmph Node Assistant (LYNA) is one of the most advanced deep learning models in the computer vision field, developed at MIT. LYNA reviews sample slides and recognize characters of tumors and metastases in a short timespan with an astonishing 99% rate of accuracy. Such a rate is hard to achieve even for the most experienced human doctors!
And let’s consider another example. CureMetrix is a California-based medical imaging company that hopes to use computer vision for medical images to help improve cancer survival rates worldwide. The company utilizes computer vision technology to build their Computer-Aided Detection (CAD) software called cmTriage. It’s the first FDA-cleared software in the U.S., intended to provide a notification triage code to the radiologist’s mammography worklist. The notification is based on the presence of a suspicious region of interest found by the cmTriage algorithm.
As it happens, this software is efficient and helps radiologists save time. According to the Breast Cancer Surveillance Consortium (BCSC), the average sensitivity of a human radiologist is 84.4%, and, on average, they recall 9.6% of cases. cmTriage offers the same sensitivity but flags only 8.2% of cases for recalling.
THE INDUSTRIAL SECTORS
Many industrial facilities can save a lot of time and human work by utilizing computer vision. How come? You see, all the oil and gas platforms, chemical factories, petroleum refineries, and even nuclear power plants operate based on tons of various sensors and measurements. And the truth is, information gathered by these sensors and cameras can be easily passed on to AI software, which would alert the maintenance staff to take safety measures at even the slightest stress or aberration detected by the image analysis and computer vision applications.
The same principle applies to healthcare–computer vision algorithms are so accurate, they can detect even the slightest aberration, possibly even unnoticeable to the human eye.
It’s a Texas-based company that helps various companies in the energy, construction, and mining industries manage their remote assets safely and more efficiently. Their computer vision systems are being used to monitor the status of critical infrastructures, such as remote wells, industrial facilities, work activity, and site security. Osperity offers remote virtual inspections and reports, proactive activity detection and accurate alerts, remote environmental inspections, and automated leak detection.
The company brags that they managed to achieve a 50% reduction in routine site visits thanks to their online reports and alerts. Moreover, there are also major financial savings. Osprey managed to decrease the average cost for an in-person well site inspection from 20 USD (the industry’s average is up to 57 USD) to just 1 USD!
As you can see, computer vision can be a tremendous simplification of everyday work in a multitude of various industries, from nuclear plants, up to grocery stores. If you’d like to implement the computer vision technology in your company–drop us a line! We will gladly show you all the benefits of image analysis and computer vision applications in your industry.
 Ryan Gross. How the Amazon Go Store’s AI Works. June 7, 2019. URL: https://towardsdatascience.com/how-the-amazon-go-store-works-a-deep-dive-3fde9d9939e9. Accessed Sep 15, 2020.
 StopLift. URL: https://www.stoplift.com/. Accessed Sep 15, 2020.
 CureMetrix. cmTriage. URL: https://curemetrix.com/cm-triage-2/. Accessed Sep 15, 2020.
 Breast Cancer Surveillance Consortium (HHSN261201100031C)
 Osperity. Field Operations. URL: https://osperity.com/field-operations/. Accessed Sep 15, 2020.
 Osperity. Customer success story: efficient well site area management with osprey reach. URL: https://osperity.com/wp-content/uploads/dlm_uploads/2016/06/Well-Site-Monitoring-With-Osprey-Reach_Case-Study.pdf. Accessed Sep 15, 2020.