Most of us are surrounded by cameras and audio-visual technologies every day. Whether we are using built-in cameras on our laptops or relying on CCTV to keep business premises safe, camera technology has become essential.
Computer vision systems and applications are taking things one step further with the addition of artificial intelligence (AI) and machine learning algorithms, including deep learning. Here is a look at 30 of the most exciting computer vision applications in 2023.
Table of contents
- What is Computer Vision?
- Computer Vision Applications
- COVID-19 diagnosis
- Tumor Detection
- Disease Progression Score
- Healthcare and Rehabilitation
- X-Ray Analysis
- CT and MRI
- Cancer Detection
- Blood Loss Measurement
- Digital Pathology
- Movement Analysis
- Defect Inspection
- Reading Text and Barcodes
- Product Assembly
- Predictive Maintenance
- PPE Detection
- The Future of Artificial Intelligence (AI) & Computer Vision
What is Computer Vision?
Computer vision gives machines the gift of sight. Part of artificial intelligence (AI) technology, computer vision uses machine learning (ML) and deep learning to help computers recognize and analyze images and videos. Convolutional neural networks have become the most common approach for image analysis, while natural language processing is more closely associated with text analysis, and generative adversarial networks focus on creating unique new content.
The overarching goal of computer vision is to mimic the way humans see and analyze what they are looking at.
The technology is being adopted in a wide range of industries, including transportation, agriculture, manufacturing, healthcare, and retail. Most computer vision systems consist of three components:
- Machine learning models to process what the cameras recorded
- Conditional programming to automate specific uses
This approach is also known as deep vision or deep vision technologies. Deep learning or deep vision employs convolutional neural networks for video processing and to develop computer vision project ideas.
Computer Vision Applications
Look around you, and you will notice that you are already surrounded by autonomous vision applications. Here is a closer look at five areas and 30 of the most specific applications within these areas.
Computer vision algorithms can help create smart cities.
Autonomous vehicles are one of the most high-profile applications of computer vision. They require a type of sight that is like human vision and use deep learning models to analyze traffic and base their actions, including autonomous navigation, on safe driving practices. Effective image classification is critical to that.
Detecting the presence of pedestrians and slowing down self-driving vehicles accordingly is one of the most critical examples of the range of applications of computer vision projects in transportation. The accuracy of these systems has recently been improved thanks to advances in sensor technology and is making city streets safer.
Parking Occupancy Detection
Understanding how many parking spaces are occupied allows municipalities to manage traffic in inner cities and guide road users to existing spaces efficiently. Rather than wasting time searching for available parking, computer vision can help alert drivers to available spaces.
Traffic Flow Analysis
Deep learning algorithms have made it possible to scale traffic management to unprecedented levels. Managing traffic flow across multi-lane highways is increasingly becoming a task associated with artificial intelligence (AI) technologies because of their capacity to analyze vast quantities of data.
Also Read: AI in Traffic Management
Road Condition Monitoring
Road condition monitoring helps identify where repairs and upgrades are needed. Once these improvements are underway, similar sensor technology can be used to detect construction vehicles and monitor productivity as well as on-site safety.
The healthcare sector was one of the first to embrace the potential of AI for high-quality imaging applications such as 3D reconstruction. Over the past few years, these applications have made their way into a wide range of medical specializations.
As the coronavirus pandemic threatened people around the world, medical images such as digital chest X-rays became essential in diagnosing cases and beginning treatments.
MRI scans based on neural networks have become sought-after tools in tumor detection. Compared to other methods, these scanners can see brain tumors earlier and more accurately, allowing doctors to start treatment sooner.
Disease Progression Score
Using computer vision technology to assign disease progression scores helps medical personnel with the triage process by identifying those who are most critically ill and therefore need support immediately.
In the case of Covid-19, disease progression technology detected more rapid or otherwise abnormal breathing patterns. As a result, the technology was able to identify ill people even within large crowds with great accuracy.
Healthcare and Rehabilitation
Imagine having access to an in-house physiotherapist when you need them. That is what vision-based rehabilitation applications deliver. As patients are training privately, computer-aided therapy monitors their performance and delivers feedback to prevent further injury and accelerate recuperation.
X-ray technology may not seem new, but AI-powered X-ray imaging has transformed what this technology can deliver. Medical professionals are currently using it for treatments and research. It has also become essential in planning surgeries.
Aside from delivering highly accurate imagery, x-ray technology is now being used to automate part of the diagnostic process.
CT and MRI
Healthcare professionals are also using computer vision techniques to analyze the results of CT and MRI scans. By now, AI systems can assess digital images as fast as and with the same accuracy as human doctors. Deep learning has also enabled manufacturers to increase the resolution of the images delivered, further contributing to accurate diagnoses.
Cancer remains a leading cause of death globally, but treatment options are improving all the time. Computer vision has become a non-negotiable for early detection of the likes of skin cancer and breast cancer. The accuracy of computer vision allows doctors to see slight differences in cells that could have been missed easily by a more traditional approach.
Blood Loss Measurement
AI-based blood loss measurement is helping to make childbirth safer. The technology allows doctors to monitor – rather than guess – how much blood a woman is losing during childbirth. As a result, medical teams can prevent critical blood loss and treat patients more efficiently than before.
Digital scanners are now capable of processing images and identifying the type of pathology in front of them. This includes inspecting tissue samples and matching pathologies to previous cases. By being presented with accurate results earlier in the diagnostic process, doctors save precious time when it comes to starting life-saving treatments.
Movement analysis techniques like gesture recognition help medical professionals detect musculoskeletal diseases and neurological problems. Initially, ML model training for human pose estimation focused on adults, but computer vision is increasingly being applied to infants.
The technology assesses a child’s spontaneous moves, which can give doctors clues about neurodevelopmental disorders and intervene sooner than before.
Aside from the healthcare sector, the manufacturing industry was one of the first to embrace the potential of AI, applied machine learning, and robotics. Today, computer vision technology has become a mainstay for various applications in the field.
Traditionally, large-scale manufacturers have found it hard to detect 100% of defects before goods leave their production facility. Leveraging computer vision and convolutional neural networks rather than human eyes alone is allowing these companies to collect data in real time and compare images against defined quality standards.
The technology has not removed the need for human-based quality control, but it is increasing the efficiency of detecting both macro and micro-level defects in goods, supporting overall error-free production.
Reading Text and Barcodes
Everyone is familiar with seeing barcodes on products we buy and use daily. The computer vision technology used to ‘make sense’ of the code is known as OCR and allows machines to detect barcodes, verify them, and convert them into readable text.
This technology simplifies identifying wrongly labeled products, alerts store managers and their employees to impending expiration dates, and is also used to track packages being shipped around the world.
Take a look at images of automobile assembly lines, and you will spot very few humans on the shop floor. Tesla is one of the leaders in the field of automation, having computerized more than 70% of all the company’s manufacturing processes.
Aside from the automobile industry, similar technologies are being used to track product components and monitor manufacturing quality as well as packaging standards.
The construction industry has been comparatively slow in embracing digital transformation and emerging technologies. But leading companies in the field are now making use of computer vision.
AI allows contractors to predict when material aging or corrosion may cause a piece of equipment to fail and schedule maintenance cycles before that happens. Predictive maintenance minimizes downtimes and supports worker safety.
PPE prevents workplace injuries, but it is all too common for workers to simply forget or remove a piece of protective equipment without thinking. AI-driven solutions can spot those lapses and remind teams to prioritize safety.
The agricultural sector may not be the first industry that comes to mind when considering AI and computer vision. However, the number of powerful applications continues to grow.
Crop and Yield Monitoring
Computer vision adds the capacity for real-time monitoring and analysis to traditional human judgment and intuition. As a result, agricultural businesses are finding it easier to detect plant malnutrition or disease sooner and protect more of their crops.
Although necessary, weeding is a time-consuming, tedious task for humans. With the cost of human labor continuing to rise in a majority of places around the world, computer vision-powered robots are far better suited to the task.
Speaking of detection, the same technology can also identify the early signs of pests invading fields. Early detection allows farming companies to deploy countermeasures quickly and effectively before major damage occurs. At the same time, the technology allows farmers to use pesticides more accurately.
Livestock Health Monitoring
Computer vision keeps an eye on livestock when humans cannot. Monitoring births, counting herds, or detecting unusual behaviors and diseases have all become part of remote animal health monitoring.
Plant Disease Detection
Like livestock, plants can develop diseases. At the early stages of growth, those diseases can be devastating. Video streams and image processing projects not only detect them but also help identify and estimate the severity of diseases.
Aerial Survey and Imaging
Agricultural businesses have grown in size beyond what humans would have imagined even a hundred years ago. ‘Walking the farm’ to detect problems is simply not an option anymore. Instead, drone surveying has become essential for larger farms.
The images captured by drones allow farm managers to assess the condition of the soil and plant health. Based on that, they can manage irrigation accurately rather than simply using judgment.
How does the retail industry benefit from computer vision? Improved customer service and faster checkouts are only two of the advantages.
Long queues at the cash counter are one of the pain points of most retailers. Computer-vision-based self-checkouts are proving to be a reliable solution for retailers looking to improve customer satisfaction.
Computer vision can make empty shelves a piece of history by alerting staff to missing products while simultaneously monitoring stock levels and supporting inventory management. Improved customer service is the result.
Intelligent Video Analytics
Intelligent video analytics such as facial recognition use image classification and image segmentation to be effective. They make it easy for store owners and their teams to detect suspicious activity and prevent theft. Current applications are already proving to be more accurate than humans because of their ability to analyze real-time movement.
Foot Traffic and People Counting
Why count people in stores? During the coronavirus pandemic, counting customers allowed staff to ensure social distancing rules were being adhered to. Today, retailers are using this technology to optimize their space and guide foot traffic to high-value products.
The Future of Artificial Intelligence (AI) & Computer Vision
The potential for computer vision project ideas seems endless at this time. As more industries are taking advantage of these technologies, vision engineers are likely to develop applications for deep learning and computer vision that we are not yet thinking of.
Convolutional neural networks will likely remain the standard for image analysis, including the analysis of video frames. Python is currently the leading programming language in the field As the volume of data produced and analyzed by these systems grows, edge devices and services can help manage the volume of data by processing closer to the end user.
Also Read: Artificial Intelligence and Urban Design
Computer vision technology and its applications are all around us, and new vision project ideas are appearing every day. Convolutional neural networks have become the industry standard for image analysis. Generative adversarial networks take the approach further by creating new content based on existing data. Alongside natural language processing, these innovative solutions are influencing our personal and professional lives at every stage of the day.