How Computer Vision Applications Answer Real-World Problems
6 Minutes Read
Do you still feel you can beat the computer bot at that game of chess? Well, think before answering. You cannot ignore the immense power of Artificial Intelligence (AI) today, which even made Gary Kasparov, the Russian chess grandmaster, lose to IBM’s Deep Blue. And then vision-guided robotics, biometrics in security, 3D printing with AI, count to be prominent computer vision applications. Right from inspecting bottles in a manufacturing production line to indexing database of digital images, or from driving automation in self-driving vehicles to controlling robots, computer vision applications form an indomitable part of every industry and are key to driving digital transformation faster.
What is Computer Vision?
A part of Artificial Intelligence (AI), computer vision refers to the science and technology aimed at building artificial systems that capture information from multi-dimensional visual data or images. To elucidate this, computer vision focuses on replicating components of the human vision system complexities, thus enabling computers to recognize and process objects in images and videos, the same way humans do. All thanks to innovations in deep learning and neural networks that AI could take great leaps in recent years. Remember the relevance of computer vision lies in the problems it can solve. It is one of the major technologies that brings about the interaction of the digital world with the physical world.
Having said that, it is worth mentioning the growth computer vision market is expected to see in the future. The market grows to USD 17.4 billion1 by 2024 at a CAGR of 7.8% during 2019-2024.
The Outreach of Computer Vision and Machine Learning
Computer Vision and Machine Learning are witnessing tremendous growth over the years due to the application of ML methods to computer vision tasks that include image registration, 3D reconstruction, segmentation, and classification, motion tracking, and object detection. Difficult computational data analytics problems are solved best by ML algorithms based on training data.
The current level of computer vision allows the detection and tracking of single objects (faces, pedestrians, cars) classes in an unconstrained setting. It enables the realization of smart cameras to identify smiling persons, pedestrian detection, surveillance applications, including image-based web searches.
Production and Management Systems in Manufacturing
The field of computer vision is known to have many applications in manufacturing. These applications are not limited to predictive analytics of machinery, accurate assembly of components, and quality control for health, safety, and products. Businesses find it productive to adopt production and management systems that use computer vision technology.
Electric vehicle manufacturers employ sensors to outfit vehicles for the self-driving function and accident prevention. Heavy industries apply machine vision to assemble each component of the hydraulic pumps with much accuracy. In addition, there are mining companies that use computer vision systems to closely monitor drilling equipment, thus identifying defects and other damage before any kind of accident.
3D printing also uses the power of computer vision. 3D printing with AI proves to be beneficial for manufacturers as well as consumers. Computer vision systems are used for detailed scanning of each layer of an object, while it is printed to rectify errors in real-time. ML predicts the behavior of materials and provides for more accurate final products based on this information.
One of the examples where the use of computer vision becomes imperative is package inspection at pharmaceutical companies. They are required to tally the number of capsules and tablets that go into any form of packaging. Computer vision for quality control in automated manufacturing systems helps to check for partially formed or broken tablets. Photos are taken as tablets pass through the production line. They are then transferred to a dedicated computer for processing images using a set of established algorithms, to check if the tablets are good for packaging.
Advanced Sensors in Defense and Logistics
Due to reduced situational awareness, a method based on passive acoustic sensors to detect vital signs in closed structures, hostage rescue, building surveillance, clearance, and search operations become challenging for law enforcement agencies and military forces. This is where pattern recognition and image processing techniques increase the functionality and performance of complex radar systems. These techniques aim to effectively and innovatively perform augmented reality (AR) based on the maintenance of mission-critical systems and field training.
In the context of new architectural and technological solutions, a compact multifunctional sensor localizes and tracks humans inside critical areas. It integrates innovative vital signs’ detection computer vision algorithms with human movement detection approaches.
A few of the typical applications of this sensor are:
Legal Investigation & Criminal Emergency: To determine the location and presence of hostages in a building.
Search and Rescue Operations: To locate and save people injured inside the building.
Fire-fighters: To quickly check on people who are trapped within the building.
The sensor uses stepped frequency continuous wave radar technology. Advanced computer vision detection algorithms allow the sensor to operate as a sensitive Doppler motion detector. It provides first responders with crucial information that makes for the difference between life and death.
Video-based Detection Systems in Road Transportation
Be it keeping an eye on pedestrians and motorists in urban areas, identifying incidents in tunnels and on highways, or gathering data on traffic, video-based sensor systems are critical. Being an indispensable part of intelligent traffic systems (ITS), video-based sensors are gaining popularity due to recent evolution in hardware and software technology.
How video-based detection systems work
A video camera or thermal imaging camera, combined with intelligent video analytics, provides an ideal solution to traffic managers across the globe to effectively monitor and manage traffic streams. An installed thermal imaging camera or video sends an input signal to a detection unit. Detection zones are superimposed onto the video image when the camera or the video image processing modules are set.
Video Detection for Traffic Applications
Traffic Light Management: Thermal and video detection technology is highly accurate and reliable. Daylight and thermal sensors offer information on waiting as well as approaching vehicles at the intersection, which leads to traffic lights turning into active management devices.
Automatic Incident Detection (AID): With the passing of every minute, there is a dramatic rise in the risk of another accident compounding the one before. Remember effective incident management totally depends on fast incident detection and verification. AID solution analyses real-time camera images and takes no time to detect all major incidents.
Safety and Mobility of Pedestrians: Pedestrian detection enables activation of road warning lights or flashing beacons. Detection-based warning signal activation is highly effective in alerting motorists and increasing pedestrian visibility.
Data Collection & Flow Monitoring: Video-based detection systems monitor traffic flow speed with accuracy to keep highways safe. This monitoring is done by differentiating levels of service: fluid, congested, or stop, dense, and go. There is the supervision of queues during roadworks and travel time is also calculated based on information flows from Video Image Processors (VIPs).
Biometrics in Security
AI has been an integral part of biometric technology. Image-based biometrics make use of AI to enable a computer system exact match a fingerprint or an iris pattern to a template on the file. Understand that face-based authentication is no longer enough to match two static images. This type of authentication requires an additional AI boost to detect signs for liveliness.
Besides, fingerprint scanning systems should be able to correct their algorithms to quickly and precisely identify the biometrics of an end-user. The line between biometrics and AI has blurred, as machine learning algorithms and convolutional neural networks deliver computer vision that can detect threats of weapons or suspicious behaviors.
The integration between biometrics and computer vision directly links to multiple applications in services and safety, cybersecurity, including biometrics for a wider control, facial recognition from a distance, forensic biometrics, 3D face modeling and recognition, intelligent video surveillance, and finger vein recognition.
Talking about IT security, varied applications of Biometrics in security comprise of home access control, vehicle access authentication, mobile phone access, and single sign-on (SSO). More and more enterprises are adopting biometrics for SSO and access control as this technology relies on recognizing someone by “who they are” and not what you have or what you know. Users are authenticated by their personal workstations or computers at the time of login, utilizing their biometric traits to access the network.
Medical Imaging in Healthcare
As AI has already found its applications in the healthcare sector, the use of computer vision has proved to be lifesaving for patients. In fact, computer-supported medical imaging application has been quite helpful for physicians. It has done wonders in surgery and curing certain diseases. Not only does it create and analyze images, but it acts as an assistant for doctors, enabling them to make better and accurate interpretations.
The application can read and convert 2D scan images into interactive 3D models. 3D modeling and rapid prototyping technologies drive the growth and development of medical imaging modalities, like CT and MRI. Computer vision application in medical imaging, thus, aids medical professionals to gain a comprehensive understanding of their patients’ health conditions.
Besides medical imaging analysis, there are many areas in healthcare in which applications of computer vision are benefitting medical professionals to effectively diagnose patients. These include precise diagnosis, timely detection of illness, health monitoring, quicker analysis of images and reports, and nuclear medicine. Computer vision technology, powered by AI, has proven to be a boom for the healthcare sector in effective patient diagnosis, right treatment prescription, and disease evolution monitoring.
Are you leveraging the potential of computer vision for your business?
As the use of computer vision systems continues to grow, the computer vision industry will act as a solution purveyor for real-world problems. Whether it is video-detection systems in road transportation, advanced sensors in defense and logistics, or 3D printing in manufacturing, computer vision systems are benefitting every sector. This technology is, indeed, worth adopting to improve on your business operations, aid healthcare, strengthen security, and manage traffic more effectively.