What is Computer Vision?
Recently, there has been a lot of enthusiasm around computer vision, fueled by decades of complex study and growing interest. The field of computer vision in artificial intelligence (AI) allows robots to see things visually. Computers may derive relevant information by combining machine learning and deep learning methods with computer vision applications and algorithms. Often uses digital photos, movies, and other visual inputs to take actions and make decisions. Machines can now perceive, observe, and comprehend thanks to computer vision. This article examines a few fascinating real-world uses of computer vision.
How does Computer Vision Work?
We need a lot of data for computer vision. It does data analysis repeatedly until it recognizes differences and ultimately recognizes different elements in the image. For a computer to learn the differences and remember a tire, especially one without flaws, it must be fed an enormous quantity of tire photographs and tire-related documents, like teaching it to detect vehicle tires.
Applications of Computer Vision
The following is a list of some of the most widely used computer vision applications in the market:
The transportation industry’s growing needs have fueled technical advancement in this field, with computer vision at its core. The Intelligent Transportation System (ITS) has emerged as a crucial area for advancing transportation effectiveness, efficiency, and safety, from driverless cars to parking occupancy detection.
Medical attention and rehabilitation
Patients recovering from sports injuries and stroke survivors need physical therapy regularly. It is quite expensive for medical service providers to carry the recurring cost of the therapist, transportation, hospital, or agency for supervision. So mobility training at home using a vision-based rehabilitation program enables the healthcare service provider to achieve very close outcomes without spending much money. Human action evaluation can be used in computer-assisted treatment or rehabilitation to help patients learn at home, direct them to carry out tasks correctly, and help them avoid additional injuries. Investigate other sports and fitness apps so that they can eventually reach their previous performance condition.
Medical Skill Training
Medical skill development On self-learning systems, expert learners’ competence levels are evaluated using computer vision technologies. They make plans for surgical training based on simulation. The concept of action quality evaluation enables the development of computational methods that automatically assess the performance of surgical trainees. People might receive feedback information that will help them advance their skill sets.
Disease Progression Score
The score for disease progression To focus medical care, computer vision can recognize severely unwell patients (critical patient screening). For example, observation is done that COVID-19 patients breathe more quickly. It is possible to accurately and discretely screen many COVID-19 virus-infected individuals for aberrant respiratory patterns using deep learning and depth cameras.
Skill Development Optimizing assembly line operations in industrial manufacturing and human-robot interaction is another area where vision systems are used. The analysis of human behavior may be used to develop standardized action models for various operation processes and assess the effectiveness of trained employees. As a result, workers’ performance may be enhanced. Production efficiency can be increased (LEAN optimization).
Autonomous vehicles are not science fiction anymore in 2023. The dependability and safety of self-driving cars are being tested and improved by thousands of engineers and developers worldwide. The computer vision integrated into the autonomous vehicle operating system identifies and categorizes things (such as traffic lights or road signs), builds 3D maps, or estimates motion. Self-driving cars use sensors and cameras to gather information about their environment, analyze it, and react appropriately.
To create real-time algorithms that support driving activity, researchers are working on ADAS technology using computer vision techniques such as pattern recognition, feature extraction, object tracking, and 3D vision.
Detection of pedestrians
Due to its potential influence on the design of pedestrian safety systems and smart cities. Pedestrian identification and tracking have become crucial computer vision research topics. Using cameras, it automatically uses differences in body posture, clothing, occlusion, lighting conditions, and background clutter to identify and locate pedestrians in photos or videos. In addition, it may find applications for pedestrian detection in areas including traffic control, autonomous driving, and efficient transit.
Occupancy detection for parking
Computer vision is already widely employed for visual parking lot occupancy monitoring in Parking Guidance and Information (PGI) systems. It serves as an alternative to more expensive, regularly maintained sensor-based systems.
Camera-based parking occupancy detection systems have swiftly attained extraordinarily high accuracy. They are nearly impervious to variations in lighting and weather thanks to the development of CNNs (convolutional neural networks). In addition, using License Plate Recognition in conjunction with Parking Occupancy Detection can also track which vehicle is parked in which space.
Quality Control Applications
Quality Control Applications for intelligent cameras offer a scalable way to integrate automated visual inspection and quality control of manufacturing and assembly lines in smart factories. Here, deep learning employs real-time item detection to outperform arduous manual review regarding detection accuracy, speed, objectivity, and dependability. In addition, AI vision inspection uses machine learning techniques that are far more reliable than traditional machine vision systems and don’t require expensive specialized cameras or fixed settings. As a result, AI vision techniques may be applied in several places and factories.
Visual Inspection of Equipment
Visual Equipment Inspection, A crucial component of intelligent manufacturing is computer vision for visual inspection. Automated PPE inspection using vision-based inspection technologies, such as mask and helmet detection, is also growing in popularity. In addition, computational vision supports monitoring compliance with safety procedures on building sites or in a smart factory.
Monitoring of road conditions
In addition to fault identification, computer vision has found utility in evaluating the state of the infrastructure by tracking changes in concrete and asphalt.
Automated Pavement Distress (PD) detection has effectively lowered the safety risk associated with accidents and improved road repair allocation efficiency.
The stability of food security is based on the production and quality of key crops like wheat and rice. However, crop growth monitoring has always needed to be more accurate and primarily based on human opinion. Applications for computer vision provide continuous, non-destructive tracking of plant development and its response to nutritional demands. When using computer vision technology to monitor crop growth in real-time, instead of manually, it is possible to identify small changes in crops caused by malnutrition considerably sooner and give a solid and accurate foundation for prompt management. Additionally, computer vision software may measure plant growth indicators and development stages.
Irrigation Management Modern agricultural output is significantly impacted by soil management. Which employs technology to increase soil productivity through cultivation, fertilizer, or irrigation. Images can provide valuable information on the development of horticulture crops. Allowing for correct estimation of the soil water balance and irrigation scheduling. Applications for computer vision offer valuable data on the irrigation management water balance. To improve decision-making for irrigation management, a vision-based system may interpret multi-spectral pictures captured by uncrewed aerial vehicles (UAVs) and calculate the vegetation index (VI).
Comparing malignant and non-cancerous cells in photos enables medical professionals to spot abnormalities and alterations. Using information from magnetic resonance imaging (MRI) scans, automated detection enables a quicker cancer diagnosis. Breast and skin cancer screening using computer vision already shows high effectiveness and accuracy.
Blood Loss Calculation
One of the leading causes of infant death is postpartum hemorrhaging. In the past, doctors had to make an educated estimate as to how much blood a patient had lost during delivery.
By analyzing photographs of surgical sponges and suction canisters captured with an iPad and driven by artificial intelligence, surgeons can now estimate the amount of blood lost during delivery.
One of the institutions to use this technology was the Winnie Palmer Hospital for Women and Babies at Orlando Health. Doctors frequently overestimate the blood volume lost by patients after childbirth. Thus, a more precise blood loss assessment that aids medical practitioners in providing patients with better care is now possible thanks to computer vision.
To monitor consumer traffic in retail businesses, deep learning algorithms can process video feeds in real time. Reusing the video stream from popular, affordable security surveillance cameras is possible with camera-based technologies. To study time spent in various locations, wait periods, and line-up times and evaluate the quality of the service, machine learning algorithms identify individuals invisibly and contactless. In-store customer behavior analytics is used to promote customer happiness. Optimize retail shop layouts, and objectively measure key performance indicators across many locations.
Interpreting barcodes and text
OCR, a computer vision technology. Is used to automatically identify, validate, convert, and translate barcodes into legible text. Because most items have barcodes on their packaging. Labels or boxes photographed can retrieve their text and be cross-referenced with databases using OCR. This process aids in detecting items with incorrect labels. Provision of expiry date information, publication of product amount information, and tracking packages throughout the whole product creation process.
Sports Team Analysis
Team Analysis in Sports Professional team sports analysts routinely analyze data to develop a strategic and tactical understanding of individual and team behavior (identify weaknesses, assess performance, and improve potential). Manual video analysis requires the analysts to memorize and mark scenes, which takes time. To provide practical team sport analytical measures for region, team formation, event, and player analysis, utilize movement analysis techniques to extract trajectory data from video content. For example, we see computer vision-based sports analysis in most major football tournaments like the Euro cup or world cup 2023.
Computer vision is set to become an even more integral part of our lives in the coming years. Within 1-2 years, we can expect to see a wide range of computer vision applications, ranging from facial recognition to self-driving cars. Computer vision technology will also improve healthcare, agriculture, and education. Computer vision will become increasingly powerful and versatile as technology advances, and its applications will become more prevalent in our everyday lives. Industries that rely on image and video data will employ computer vision and adopt the AI-first strategy to transform businesses into more efficient, accurate, and predictable ventures.