Both private and public organizations are striving to shift toward automated data processing. However, the nature of data collected by businesses has especially evolved over the last few years. It has grown to include optical or visual components as well, such as images and videos. This has greatly complicated data handling and processing tasks.
It would be impossible, not to mention highly inefficient, to manually process and derive insights from the large volumes of data that business systems and consumers produce. Thus, to utilize this data to its maximum potential, business owners turn to machine learning-based computer vision solutions.
Having delivered over 80 projects involving cutting-edge artificial intelligence (AI) applications, we can attest to the fact that computer-aided data processing yields noteworthy benefits concerning efficiency, productivity, and profitability.
Let us shed some light on what computer vision (CV) is, the different types of CV tasks, and the many ways in which businesses operating in various industry verticals implement CV applications to gain numerous advantages.
In this article, we are going to cover the following topics
Computer vision is a technology that aims to teach computers how to interpret optical data such as images and videos. With machine learning-aided computer vision, computers can now perform various data processing tasks with minimal reliance on human input or intervention.
What started out with complex algorithms performing basic tasks involving image processing has now evolved into computer vision as we know it. The technology has gradually advanced to bring computers’ visual processing capabilities closer to or, in some cases, even beyond those of humans.
Within much shorter time spans, computers can quickly adapt to new processes, perform specialized tasks, and learn and identify patterns in large volumes of visual data with increasing accuracy.
Today, businesses operating in retail, manufacturing, automotive, security, utility, healthcare, and a multitude of other industries are heavily reliant on computer vision applications.
How machine learning contributes to computer vision
Computer vision is a subset and application of machine learning. Computer vision systems derive their processing and learning capabilities from machine learning and artificial intelligence algorithms.
“If AI enables computers to think, computer vision enables them to see, observe and understand.” – (IBM)
While the principles are borrowed from machine learning systems, the specific methods differ since computer vision tasks involve solely optical data. Computer vision solutions make use of deep learning algorithms, mathematical and statistical models, and certain hardware such as cameras and sensors.
How computer vision works – Tasks and activities in CV
Computer vision machine learning is a two-step process that solves problems in the same way that other machine learning algorithms do. The first step is learning and the second one involves prediction.
The two-step process
In the first step, the machine is fed a certain amount of data (visual data in this case) for training purposes. The “learning” can happen either independently or in a supervised environment.
For supervised models, the training dataset is labeled or tagged. For example, if you want to teach a computer to identify pictures of roses and daffodils, you will first input some images of the two types of flowers. You will tell the computer whether each image contains a rose, daffodil, or neither. The input data will also contain certain tags or characteristics for each image.
The computer will then build statistically meaningful relationships between these features to identify patterns.
When implementing an unsupervised learning model, the training data does not contain the answers. It will simply contain a mix of images of roses and daffodils. It may or may not contain additional features needed to help identify each type of flower.
The machine will go through these images, pick up on the similarities or differences between the images, and group them together into clusters based on these similarities.
This model works well when you are looking to identify patterns or a structure where you can’t see any at first glance.
In the second step, the algorithm is fed test data. It utilizes all the learning it has done in the first step and makes predictions about this fresh data. This determines the correctness or accuracy of the associations or patterns that the machine learned in the first step.
Subtasks in computer vision machine learning applications
A single computer vision task aiming to solve a larger problem can require the machine to first perform multiple smaller subtasks that address certain parts of the solution. Let’s go over some of the tasks that a typical computer vision machine learning solution might be expected or trained to perform.
Image recognition (aka image classification): This is one of the simpler tasks performed by CV algorithms. Image recognition tasks identify images containing a certain type or “class” of objects, people, places, or other features, by studying their visual contents.
Object detection: This task involves not only the identification of one or more types of objects in a given image but also their location, i.e., exactly where they are present in the image. The identified objects are assigned bounding boxes to indicate their position.
Image segmentation: This can be seen as a more precise version of object detection that identifies exact object edges. It conducts a pixel-by-pixel analysis of an image to determine which pixels are occupied by which particular objects or regions (backgrounds).
Video recognition: Extrapolating image-based object recognition to video data leads us to video recognition. Here, analyzing multiple consecutive frames can be used to gain a better understanding or context of what is going on in the image.
Object tracking: Also known as video tracking, this enables the machine to not only detect different types of objects within the initial image (frame) but also track their presence and trajectory in subsequent frames in a video.
Computer vision machine learning use cases
The applications of computer vision are not restricted only to small businesses, large enterprises, or public authorities. CV solutions have become a household presence, with benefits ranging from enhanced safety and security to effective medical diagnostics. Here is how different user bases are leveraging computer vision to change our lives for the better.
The field of biometrics has seen great improvements when combined with computer vision machine learning. Digital systems used for facial recognition, fingerprint-based identification, and retina scans are becoming increasingly precise owing to deep learning integration. A common example of this application is seen in smartphone protection where devices can correctly identify owners viaface ID.
Public and private spaces have become safer with advancements in computer vision. Using live video feed from CCTVs, computer vision applications can detect people exhibiting suspicious behavior or carrying dangerous objects such as weapons.
Vehicles on the run can be identified and tracked via their color, brand, and even license plate number. Advanced biometric identification systems can now be used to identify persons of interest and track their movements. The same technology can also be leveraged to locate missing persons or people who suffer from medical conditions such as dementia.
Today, a wide range of healthcare systems are greatly dependent on computer vision. Diagnostic imaging systems, such as X-rays and CT scans, used by healthcare providers have become better at visualizing internal organs and detecting diseases and complications. The systems apply CV concepts such as image segmentation and classification to learn from thousands of sample scan results and establish patterns. This has resulted in faster, more accurate, and timely diagnoses of serious illnesses such as cancer, hemorrhage, or other previously undetectable internal injuries.
Human pose estimation, a computer vision object tracking task, is used in fitness applications. As its name suggests, this mechanism detects the stance or posture a person has assumed while exercising and alerts the user in real-time when it sees an incorrect pose. This can be incredibly helpful not only in critical physiotherapy exercises but also for regular fitness training.
Computer vision has also made a significant dent in animal healthcare. Arecent study proposed the usage of CV-powered motion monitoring systems to detect and reduce the spread of swine fever, among other dangerous outbreaks. An artificially intelligent vision system can be used to observe and monitor the behavior of animals within a closed environment, and identify those exhibiting limited movement (or other symptoms) due to sickness. This solution has the potential to catch infectious diseases early on and prevent the loss of precious lives.
Businesses that wish to achieve greater automation levels, efficiency, productivity, and quality control choose custom computer vision machine learning solutions. There is a wide range of industrial applications of CV, ranging from remote production line and packaging process monitoring to predictive maintenance of industrial equipment and worker safety checks to prevent accidents.
Here’s a real-life example from a project that MindTitan worked on for Hepta Airborne, a provider of AI-based, drone-operated power grid management and analysis services. The drones aerially monitor the power lines and capture images from a safe distance. This optical data is fed to a computer vision system that detects faults within power lines and helps the operators to take timely actions, reducing disruption and outages in power supply while lowering maintenance and inspection costs.
On the other hand is retail, which is also gradually embracing a variety of computer vision solutions to not only provide better experiences to customers but also yield higher sales and profits for sellers. Applications include cashierless checkouts to eliminate queues, automatic detection of empty store shelves leading to faster restocking, and analyzing in-store customer behavior and sentiment to offer quality services and boost revenue.
Traffic and crowd analysis
Computer vision machine learning can be seen in action in crowd and traffic surveillance to ensure public safety and security. Various types of objects, such as cars, large vehicles, cyclists, and pedestrians can be detected and their behaviors observed using object recognition and tracking. Densely crowded places can then be strategically and efficiently managed and controlled to avoid accidents.
One application is seen intraffic management systems using computer vision. Modern systems are capable of replacing traditional fixed-time traffic lights with an optimized mechanism that automatically calibrates stoplight waiting and running times depending on the detected vehicle load.
Another computer vision application that has been trending recently in the automotive industry is autonomous/driverless cars and advanced driver assistance systems. Modern self-driving vehicles now come equipped with a plethora of sensors and cameras that capture live feed and, based on the surroundings, map out a safe, suitable route from source to destination using advanced object tracking algorithms.
Thanks to computer vision, robots no longer have to rely merely on sensor data. They can now effectively “see” their surroundings too. Consequently, they have refined their navigation and functionality. CV-driven robots have come to play a pivotal role in many industries such as healthcare and manufacturing, performing at astonishing levels of accuracy and slashing labor costs.
Computer vision is now a widespread phenomenon not only in industrial robots but also on a smaller scale in the form of home robots. A great example is the Roomba, which uses cameras and sensors, coupled with artificial intelligence, to automate cleaning and vacuuming around the house. Equipped with 360-degree vision, they are capable of mapping house plans and identifying furniture and fixtures, and successfully avoiding them while cleaning.
A major achievement was recently celebrated by the pet community when it was announced that the Roomba j7+ comes with a built-indog poop detection and avoidance mechanism. Before this development, Roombas were unable to detect animal waste (as well as other dangerous objects such as cables). This would result in them spreading pet waste all over the house, creating an unsightly mess for their owners to manually clean.
Final thoughts: How to build a custom CV solution to meet your business goals
Computer vision machine learning has empowered businesses big and small to solve complex problems by automating the processing of visual data, i.e., images and videos. CV algorithms learn from past experiences to solve modern problems by viewing data from a fresh perspective. They are capable of discovering patterns hidden in data that might be too obscure to be spotted by the human eye.
The expansive capabilities offered by computer vision have encouraged its integration within everyday business processes as well as large-scale applications. From security, surveillance, and transportation to healthcare, manufacturing, and retail, the applications of computer vision span far and wide. Both private and public sector organizations are now readily adopting CV solutions.
Each computer vision project that we deliver is custom-built to address a specific business problem, which leads to better, accurate results. We follow a time-tested process based on a few simple steps.
The first step to building a customized computer vision solution comprises a meeting with the client, where we discuss how the envisioned CV project will add value to the client’s operations and processes.
At this stage, we also make it a point to address concerns related to data privacy and security, which most clients express when it comes to CV projects as huge amounts of sensitive data are often exchanged during development. We are open to making on-premises arrangements with the client and adhering to dedicated data access regulations. In case of a lack of training data, we can set up the necessary processes to convert the existing data into a dataset suitable for this purpose, or we can determine whether any other openly available sets of visual data can be used.
Next, we agree on team structure. A typical development team assigned to a computer vision project consists of a project manager, analyst, data scientist, and machine learning engineer. Depending on your project size and scope, there can be any number of experts added to the team.
The development team begins by perusing the dataset available for training the custom application to obtain specific results. The viability of the data is tested against certain parameters. The machine learning model is built to perfection by conducting multiple accuracy tests using fresh data.
There are two possible deployment mechanisms for the last phase of the process. Many clients wish to integrate the newly developed AI pipeline within their existing systems, either in the cloud or on-premises. If you don’t have any such challenging requirements, we can deploy it in the form of a user-friendly web-based or mobile application, effectively hiding the technologically complex machinery and presenting a clean interface to end users. You and your team will be good to go!