Until recently, computer vision had little to do with our everyday reality. Although the idea had been in development since the 1950s, it was the thing of sci-fi movies for most people. But this situation has changed. Today, computer vision algorithms are an integral part of many modern software solutions, and the level of adoption is constantly growing.
Based on current growth, the global computer vision market is projected to reach $21.17 billion by 2028, a CAGR of 6.9% from 2021.
This article will explain what this technology is about, outline techniques it’s built on, and provide the most common computer vision examples. We’ll also talk about the technology’s areas of application and the implementation challenges you may have to overcome. First things first — let’s begin with a definition.
What is computer vision?
Computer vision (CV) is a subdiscipline of artificial intelligence (AI) that enables computer systems to analyze and interpret visual information. For instance, modern CV systems can use data directly from cameras and thermal sensors or process ready data sets. The idea behind CV is quite straightforward: we want machines to identify real-world objects. We also want them to make decisions based on visual data quickly — preferably in real-time.
But how does computer vision work? First, you have to provide a CV algorithm with a visual reference, and you need to do it in numbers since computers aren’t good at creative thinking. Imagine you want your CV-based system to be able to identify an image of a mouse. Here’s how things will go:
- You find an image of a mouse
- Label it “mouse” and encode it for the system pixel by pixel, including color information
- You repeat this with a few thousand images
- The CV system analyzes the data and discerns patterns. Now your system has the definition of a mouse
- The system applies the definition to a new image, checking frames for patterns and detecting mice in real-time
This, of course, is a simplified scenario. Today’s computer vision technology has come a long way and no longer relies on manual work. AI uses machine learning (ML) and deep learning (DL) to identify objects better.
With ML, parts of software employ algorithms that can automatically identify patterns. It allows CV systems to detect and classify objects. DL is a more advanced subset of machine learning. It enables AI to extract recurring patterns without the help of humans based on large sets of data. Feed the system hundreds of thousands of images, and the neural network will crack the code and produce a mathematical equation that describes an identification pattern.
Now that we’ve touched upon the concept of CV in general, let’s get down to the techniques that make up its core.
Computer vision techniques that are changing the world
CV systems utilize sophisticated analytical tools to provide us with several critical functions. Here’s a quick overview.
To understand how image classification is used in computer vision, think of the last time you had to prove you’re human by going through a Captcha check. Remember how you were asked to select all images with motorcycles in them? Well, we want CV systems to be able to do that on the fly, too.
There is a data set where items are split into labeled categories: by type of object, color, size. We need the computer vision application to look at these categories (classes) and extrapolate the classification approach to a new group of images. Basically, to make AI go through a Captcha test.
To do that, data scientists often use deep convolutional neural nets. It goes like this:
- Data scientists feed the system a data set with images that are labeled and split into classes
- They keep doing it repeatedly to define a classifier — a characteristic that defines a class
- Then, they take an unknown set of images and ask the CV system to classify them
- Finally, data scientists compare the results against the actual class labels of this new set, make corrections, and repeat.
The goal is to receive a system that classifies images with the highest degree of accuracy. So we can use those classes for our next task.
When we know what type of objects to look for in an image, all we need to do is to label them correctly. For a Tesla autopilot, this means categories like “car”, “truck”, “road sign”, “pedestrian”, etc. If we had unlimited resources, we could simply scan the entire image pixel by pixel. But what if it’s a live high-res video feed?
The solution is simple: identify areas with uneven texture since they are more likely to contain objects than the background. From now on, the computer vision algorithm will only work with those regions, saving on computational power — especially because we’ll need it for the next technique.
If you’re into photography, you’ve probably come in contact with this technique. When you select object tracking in your autofocus settings and mark the object, your camera will keep focusing on it as long as it’s in the frame.
Data scientists use two general approaches for tracking objects:
- Generative. This means the system has to identify the object by its distinctive features. Reliable, but taxing.
- Discriminative. In this method, computer vision is used to discern the object from the background. As it is robust and resource-savvy, this method is becoming the industry standard for tracking.
Engineers can use resources like a Deep Learning Tracker to train neural nets. But new methods are constantly being created to improve precision and exclude distractions in the image. But there’s yet another level.
In addition to classification, where a CV system is able to define classes of objects, it also needs to detect their edges. Segmentation is a technique aimed at identifying on a pixel level where the sky ends and a building begins, where a pedestrian’s figure overlaps with a car, and so on.
This task requires significant resources, and engineers tackle this problem by using fully convolutional networks. Because applying this method to a full-resolution image is too greedy on resources, the technique uses a combination of downsampling and upsampling. But we still need to go a little deeper.
This technique uses a state-of-the-art Mask R-CNN framework to distinguish between instances within classes. Example:
- A computer vision system identifies that a class named “cars” is present in the image
- By applying instance segmentation, the system can mark each car in the class with masks of different colors
- Data scientists can use this information to individually track each of the objects, retaining their unique characteristics
These are the five main computer vision techniques that deliver most of the functionality of CV systems.
Computer vision technology is used for multiple purposes, from face recognition to emergency response services on social media. A self-driving Tesla, an early cancer diagnostic tool, and a military radar system — all exist thanks to CV, too. So, let’s go over the real-world use cases of CV.
Most common applications of computer vision across industries
Computer vision technology is thriving in today’s technogenic world. First of all, humans create ever-growing amounts of visual data. For example, 28 billion images and videos are uploaded daily to Google Photos alone. Huge public data sets are also available. Secondly, computational resources have become cheaper and more advanced, enabling the building of powerful hardware solutions for CV. Finally, data scientists are constantly hard at work with computer vision in high demand, developing more efficient computer vision algorithms.
Advances in computer vision technology have transformed various industries. We’ve picked a few where the impact is most visible.
The most prominent benefits that computer vision brings to the automotive market come from object detection and classification. Data from external cameras, combined with ones monitoring the driver, can prevent accidents and help achieve new standards of safety on the road.
One of the most advanced car manufacturers of today, Tesla has clearly shown how cutting-edge tech can revolutionize the industry. The advent of self-driving cars seems inescapable. For the autopilot to work, vehicles require computer vision-enabled hardware, while manufacturers absolutely need those extensive data sets for analysis.
The UN predicts a 70% increase in demand for agricultural products by 2050. The industry needs to step up its technology game with global warming advancing, and CV is the go-to option. Use cases of computer vision in agriculture include:
- Product quality control
- Livestock monitoring
- Better grading and sorting
- Early detection of anomalies
- Prediction of weather conditions
- Crop model calibration
Deep learning can help create much better models for agricultural businesses, which will ensure a sustainable future of smart agriculture.
When it comes to financial services, computer vision has a lot to offer, especially in terms of facilitating digital transformation. Banks and other financial institutions can expect improvements in the following areas:
- Digitization of documents — for faster processing and data extraction
- Face recognition — for better security and Know Your Customer services
- Damage assessment — for insurance companies, for both automotive and real estate
Computer vision technology can produce lots of valuable data that banks can use to streamline their operations and improve customer experience.
Healthcare professionals often find themselves in situations where every minute counts, so computer systems that can offer speed and reliability are highly valued. Computer vision technology gives doctors the right tools for making better-informed decisions.
For instance, CV helps detect neurological illnesses by analyzing CT scans. It’s also used for the analysis of X-ray images and ultrasonic scans. By using this technology, oncologists can diagnose cancer with greater accuracy, as well as discriminate between malignant and benign tumors.
Real-time tracking tools provided by CV are immensely helpful for scoring athletes’ performances. Instead of relying exclusively on the eyesight and memory of judges, we now have an objective means of monitoring every parameter we might need.
Collecting performance metrics is also invaluable for training. The experience of trainers can’t be underestimated, but augmenting it with first-hand data can lead to even better achievements.
Thanks to efficient monitoring and robust automation, computer vision systems can streamline many aspects of production. For instance, Quantum’s quality control CV solution helped the client cut manufacturing losses by achieving 99.99% accuracy. In comparison, the previous solution was only able to deliver 10% of that performance.
Other uses of CV in manufacturing include vision-guided robots, packaging control, and labeling. By analyzing the data collected in these processes, manufacturers can detect pain points and arrive at decisions that will impact their business operations on a larger scale.
Combined with IoT sensors, CV allows the creation of fully automated logistical solutions. Here are some of the benefits of using computer vision systems for supply chains:
- Accurate detection of labels, barcodes, and package dimensions
- Identification of damaged goods
- Round-the-clock monitoring
- Detection of missing items
- Enhanced sortation
- Optimization of space in transport
With a well-designed CV solution in place, a logistics company can achieve 100% traceability across the entire cycle.
CV can help emergency services by providing real-time analysis of satellite shots. This can be critical for planning when responding to wildfires, floods, earthquakes, and other disasters. With the quick and accurate analysis by AI, managing humanitarian aspects of catastrophes becomes easier as well.
The use cases we’ve mentioned are by no means an exhaustive list of the possible applications of computer vision systems for all industries. But there are a number of difficulties you may encounter when implementing CV tech.
Challenges of computer vision
Implementing computer vision systems is a serious endeavor that requires proficiency in software engineering and knowledge of the relevant hardware. Here are some of the problems your CV solution can suffer from:
- Hardware limitations. There’s always room for improvement with techs, like camera sensors and server hardware. If your work requires the highest quality of imaging in poor lighting conditions or real-time data processing, you should invest in the right hardware from the start.
- Poor quality of input data. For optimal training, deep learning models require high-quality data sets, obtaining which can be costly, especially if you’re running a niche business and public data sets aren’t available. But there is a solution — you can use programmatically generated synthetic data, which can be up to ten times cheaper.
- Failure to choose the correct architecture. It might be tempting to choose an architecture model that can help deliver your final product within a shorter time frame. However, you should always consider the risks associated with costly rework and failure to achieve business goals. Carefully assess your current tech maturity and communicate your expectations to your software vendor, so you can come up with the best strategy.
- Scalability issues. This issue is basically a combination of poor initial choices of hardware and architecture. When it’s time to grow, your infrastructure shouldn’t be the bottleneck. And while cranking up on your cloud resources is generally possible, it’s much harder to fix the inherent scalability restraints of software.
The good news is that most of these are technical issues you shouldn’t run into with a reputable software partner.
To reap the numerous benefits of computer vision, you’ll need to properly set up deep-learning algorithms and feed them lots of quality data. The next step is developing a solution that will be custom-tailored to your objectives while having the potential to scale up easily.
Working with an experienced software company like Quantum is your guarantee of the best possible outcomes. We’ve spent the last 15 years developing bespoke computer vision solutions, mastering the craft of deep learning and data analysis. Feel free to browse through our portfolio, and don’t hesitate to contact us with any questions.