CV (computer vision) is a field in AI which enables systems to derive consequential data from digital videos, images and any other ocular inputs. CV helps in not only taking action but also in making recommendations depending on the obtained information. AI allows computers to think and take actions, while computer vision helps in seeing, observing and understanding.
The market for CV has intensified over the recent years because of its increased applications across different industries. According to Allied Market Research, the international computer vision market has grown over the years. In 2020, the CV market will result in approximately $9.45Bn and is estimated to reach $41.11Bn by 2030. The sector reported around 16.0% CAGR from 2020-2030. Self-driving cars are one among the most significant examples of successful computer vision applications in 2023.
Computer Vision
One of the Artificial Intelligence (AI) sectors is Computer Vision (CV), which uses Deep Learning and Machine Learning (ML) models to see, recognise and analyse things in videos and photos in a similar way as humans do. Computer vision is becoming a trend in Artificial Intelligence for different applications like vision inspection, automation and remote monitoring.
The popularity of computer vision applications is that they are widely implemented by brands and have a massive effect on them throughout different industries ranging from security to retail, automotive, logistics, healthcare, agriculture and manufacturing.
Also Read – What is computer vision? Some real-world examples, applications & advantages
What are computer vision (CV) systems?
CV systems are a setup that use cameras to obtain visual data, ML models for image processing, and conditional logic for automating application-specific case use. Edge Intelligence (deployment of AI into edge devices) facilitates implementing CV’s efficient, scalable, private and secure application.
Prominent areas of computer vision application
The potential of computer vision applications is sky high. Over the years, computer vision systems have been widely accepted as they have helped minimise the burden on humans and their errors in various processes. There are several sectors in which computer vision application has intensified over the years. Some of these sectors include:
Manufacturing
- Equipment’s visual inspection
- Productivity analytics
- Skill training
- Quality Management
Healthcare
- Covid-19 detection
- Cancer detection
- Cell classification
- Mask detection
- Movement Analysis
- The score of disease progression
- Tumour detection
- Rehabilitation
- Training in medical skill
Agriculture
- Farm automation
- Animal monitoring
- Crop monitoring
- Plantation monitoring
- Flowering detection
- Detection of plant disease
- Insect detection
- Automatic harvesting
- Automatic weeding
- Agricultural testing of product quality
- Monitoring UAV Farmland
- Irrigation Management
- Yield Assessment
Transportation
- Detection of moving violation
- Vehicle classification
- Analysis of traffic flow
- Detection of parking occupancy
- ALPR (“automated license plate recognition”)
- Re-identification vehicle
- Detection of traffic sign
- Pedestrian detection
- Monitoring of road condition
- Systems of collision avoidance
- Assessment of infrastructure condition
- Monitoring of road condition
- Detection of driver attentiveness
Retail
- People counting
- Customer tracking
- Analytics of waiting time
- Theft detection
- Social Distancing
Sports
- Motion capture in a markerless manner
- Tracking of player pose
- Pose tracking of multi-player
- Performance assessment
- Stroke recognition
- Analysis of sports team
- Coaching real-time
- Ball tracking
- Sports event detection
- Highlight generation
- Activity scoring
10 prominent computer vision applications to watch out for in 2023
1. Quality Control in Factories:
Manual inspection of the produced goods is completely resource intensified and manual to a greater extent. Computer vision can be used to implement a scalable, automated visual and quality control system. The deep learning prowess stands robust against the traditional machine vision systems as implementing the computer vision won’t require expensive equipment and real-time detection to find faulty products.
2. Skill Training for Workers :
Skilled labour will always be crucial for any assembly line process; computer vision; can be achieved efficiently. The human-robot interaction can result in the implementation of standardised models for various steps, which can help to evaluate the worker’s performance. Moreover, it is beneficial as it helps provide efficient productivity and smoothen the process by lowering the accident rates.
3. Covid-19 Diagnosis:
Darwin AI, Canada, has taken the revolutionary computer vision to a notch higher. A series of chest x-ray radiography (CXR) images were created under the name of COVID-Net to detect COVID-19 with the help of computer vision.
4. Mask Detection:
The past few years have shown the necessity for masks to curb the deadly virus globally; companies like uber have implemented the computer vision features such as face detection on their app. The app will detect and allow entry for the passengers wearing masks. These are not limited to cab bookings but can also be implemented for public transportation to contain the virus’s spread.
5. Automatic Harvesting:
Harvesting a crop is one of the most laborious and high costs as it requires a lot of reliability in mechanical operations and manual harvesting. Though, the recent revolution in computer vision has reimagined the harvesting scenario. High-end harvesting machines and picking robots are based on computer vision technology. The main area of attention is to ensure the product quality during harvesting to get the best value in the market. Examples of such implementation can be seen in automatic cucumber picking and automatic identification of cherries.
6. Crop Quality Testing:
The quality of the crop produced is the most significant factor which affects its market value and results in higher consumer satisfaction. Computer vision has provided an effective way to perform external quality checks of the products to maintain and monitor their healthy harvest. These computer vision systems are mainly used for rapid testing of lemon damage and non-destructive evaluation of potatoes.
7. Moving Violation Detection:
There has been an increase in traffic, and with an increase in traffic, it has been next to impossible to maintain and detect law violators easily. Computer Vision has been a saviour in such situations where law enforcement authorities and municipalities have an increased installation of camera-based motorway monitoring systems, which should result in curtailing unsafe driving behaviour. The implementation is not limited to monitoring hazardous diving but extends to detecting a stopped vehicle in a prohibited or dangerous spot.
8. Traffic Flow Analysis:
Traffic flow analysis has been studied for a long time to create an intelligent transportation system (ITS). These are carried out using various methodologies such as tags, under-pavement coils, cameras and many more. Out of these, the usage of the camera to analyse the flow is non-invasive and, when powered with computer vision, provides us with a large dataset which can result in the creation of a successful ITS.
Interesting Read: 6 Most Innovative Implementations Of Edge AI
9. Goal-Line Technology:
Football is fast-paced and requires much precision to decide if the ball has passed the goal line to be counted. Implementation of camera-based systems which implement computer vision has been crucial to determining goals which can put referees in a tight spot; these don’t require any additional equipment that is non-invasive to be implemented. Implementing such would only require high-speed cameras whose captured image can be used to triangulate the ball’s position.
10. Ball Tracking:
The real-time object tracking technology powered by computer vision can detect the ball’s patterns and trajectory data. These datasets can be fruitful for evaluating player performances and analysing game strategies. One of the significant implementations can be seen in sports such as football which require a large field; with the help of deep and machine learning, ball tracking can lead to in-depth and crucial game analysis.
Conclusion
In the past few years, AI has come a long way. Amongst all the sectors of AI, Computer vision is the one stream that uses Machine Learning and Deep Learning to recognise and analyse pictures and videos similar to how humans perceive and process them. With increased development of computational power and abilities of AI, computer vision has been evolving to become an integral part of our daily life.
Statista states that the worldwide computer vision market has seen significant growth. In 2019, the global health market based on computer vision AI was expected to be worth 1.6 billion USD. Companies like Google, IBM, and medical institutions like NHS or Mayo Clinic are amongst the major promoters of AI-related solutions.
Moreover, the Computer Vision industry in China has already reached over 207 billion Yuans. It has been projected to expand at an average rate of 19 percent leading it to become a 640 billion Yuans worth industry by 2025. In 2020 alone, the computer vision market in China covered 57 percent of the total AI market. These stats are nothing but a glimpse into the future of Computer Vision (CV) which is projected to multiply as we culminate in our daily lifestyle.
Above mentioned applications list out only a few successful implementations of Computer Vision that we see in our daily lives. Soon enough, with given time and innovation, we would see Computer Vision (CV) do wonders and resolve challenges that could look trivial and requires tremendous labour in today’s time.
