Machine Learning has emerged thanks to a huge improvement in worldwide computational power, data digitization, analysis, and processing. With the rise of these new technologies and top-notch approaches to understanding human biological and psychological abilities, computer vision branched out creating a new market and business opportunities for startups and corporations.
As the world is getting better at understanding how humans recognize images, the most innovative companies are taking the first steps in creating image recognition applications and devices to tackle the most common problems with new technologies.
Image processing has already touched a number of industries where it makes significant improvements in productivity, efficiency, reducing human errors, impacting the working processes, and changing the world for the better.
Today, computer vision is already influencing agriculture, automotive, industrial, energy and utilities, and other sectors which are of the most importance for the world’s well-being and prosperity.
In this article, we will look at the most effective computer vision (CV) use cases in the industry and how they are impacting the world.
Computer Vision in Agriculture
Agriculture is one of the most popular sectors for CV solutions implementation. Machine Learning is becoming an integral part of accurate yield mapping, yield estimation, disease detection, crop management, and harvesting using multitemporal remote sensing imagery processing, soil analysis technologies, and automated harvesters which are thoroughly described in an MDPI publication on Machine Learning in Agriculture.
Crop Insurance
Program-Ace has taken part in developing a drone-based agricultural solution which detects crop infrastructure damages using an environmental camera with image recognition and processing methodology.
A smart drone scans the crop and soil and sends the information to the cloud-based platform which looks at the variety of crop parameters such as texture, color, dynamics of growth, pests, and diseases then determines the quality of the crop using a neural network.
Also, the drone uses an AR-implemented solution which allows field operators and farmers to detect the damaged crop area. Using that information about the crop quality, farmers are able to prevent crop failure and save a lot of food and money.
Food Quality Inspection
As a part of Computer Vision, image processing is finding a great use in agriculture, too. There are many computer vision uses cases of image processing in agriculture from fruits and vegetables category grading using the information about the plant shape, size, and color. Using a camera, photo sensors, color threshold dynamics, and NDVI parameters, an application is able to determine which fruit or vegetable should be sorted. If the fruit is damaged, contaminated, or has any other defects it will automatically be sorted.
According to ResearchGate, the accuracy of color threshold accounted for 88% of sorting precision while overall grading was 94%. This level of accuracy in image recognition and processing can revolutionize the food quality which is delivered to our table as well as reduce the food waste, improve the production efficiency, and reduce costs.
Computer Vision in Automotive
From stereo and motion analysis for driver assistance to self-driving cars and automated car renting, computer vision is blurring the line between the present and the future. Cars are now able to see whether the driver is tired, closes their eyes, fatigued, etc., to prevent the number of car accidents and increase safety on the road.
Thanks to the technological advances in real-time object detection and tracking such as YOLO, developers and creators are making huge steps forward in computer vision improvements. Such systems are the foundation of behavior analysis on the road such as behavior of pedestrians, cars, animals, etc., which will help self-driving cars process the incoming data much better and make automated decisions accordingly.
Tesla Autopilot
Tesla Model X has introduced an advanced autopilot system which helps a driver operate the vehicle better and more easily. The Enhanced Autopilot costs $5,000 at the time of purchase. The new Tesla model also has a self-driving capability which is able to recognize things such as lights, pedestrians, cars, and objects.
Not only can the self-driving vehicle drive safely, but it can also park professionally helping busy people reduce their parking time. The cost of the self-driving capability is $8,000. In contrast, the autopilot and self-driving features can be activated later for $6,000 and $10,000 respectively.
Computer Vision in Healthcare
In healthcare, computer vision is used for a variety of medical purposes. For example, using machine learning technologies scientists are able to teach computers to recognize tumors and build 3D models of it to target the disease more precisely. Other examples are heart and brain abnormality detection, and prescribed treatment plan regulation.
Microsoft Kinect: Stroke Recovery
By combining the two methods of gamification and motion sensing, Microsoft has developed an interactive, home-rehabilitation system for motor recovery after a stroke. The system uses the Kinect sensor’s 3D camera to capture the movements of a patient undergoing therapy. The stroke recovery system collects the data such as the coordination level, manual dexterity, hand trajectory, reflex and reaction abilities to measure and track the progress of a patient.
Program-Ace focuses on similar projects for medical centers and institutions for educational purposes.
Program-Ace Movement Tracking
At Program-Ace, we are currently working on a real-time body movement tracking system which uses the OpenPose library. The system will be used as a part of a fitness application for performing a set of exercises following a fitness instructor’s movements. The instructor will be showing what exercises a person should do.
The camera tracks a person’s movements and matches them to the instructor’s movements. The app will then show how accurately the exercises are performed in order to increase training effectiveness, healthiness, and accuracy.
Computer Vision in Symbol Recognition
Symbols, handwritten texts, and digits are easy to recognize for humans, except for some captcha, of course. However, symbol and digit recognition has been an impossible task for computers. Because of the variety of handwriting and features it’s very hard for a computer to identify what’s written. Fortunately, today it’s possible and can be used for a variety of industries such as criminology, cryptography, and linguistics.
Now, using TensorFlow or CoreML, one is able to build anything from simple decision tree classifier to a neural network for teaching machines to interpret human speech and handwriting and then do some cool things with it.
Program-Ace Image Data Decryption
In cooperation with another company, we’ve built an image decryption application. The application uses image recognition technology to recognize hidden patterns on a sheet of paper and extract digits or another image’s encrypted data. The application is under NDA, so if you would like to know more information about it, contact us.
To give you an idea of this application, the same image data decryption technology is used for recognizing barcodes or QR codes.
Computer Vision in Security and Surveillance
One of the use cases of computer vision is a variety of security and surveillance purposes. Many big companies, startups, and individuals are working on developing advanced security systems using computer vision technologies. Today, security systems are able to detect, track, and notify people about burglar interferences, terrorism in public places, and more.
Ron Picard: Computer Vision Security System
Based on the open source computer vision library (Open CV) in combination with Python, Ron Picard has developed a computer vision security system which can detect a burglar in a house, record a video, and send an email security alert containing the video. The high-quality video helps to understand who broke into the house and minimizes the level of false alarms which are often sent to police departments.
Microsoft Safety Surveillance
In 2017, Microsoft introduced safety cameras which are able to recognize the incidents and hazards in an industrial plant and instantly notify the workers who are responsible for the plant’s safety or those in a potential danger area.
The diversity of the system allows it to be used in hospitals to track patients. It’s useful when a patient has been prescribed to stay in bed but is now more active than necessary. The system notifies medical staff to take the needed actions. It’s a simple surveillance system, and yet it’s able to solve very complicated problems.
Conclusions
As a part of machine learning, computer vision is rapidly transforming various industries to improve the quality of life in today’s world. Many ambitious and professional developers are making impressive advances and innovative solutions to the common problems.
At Program-Ace, we are passionately following the trends to help improve the world. Being a Ukrainian full-cycle software development company and having the most professional developers, we are able to deliver cost-effective computer vision solutions in a variety of industries.
Have an idea in mind? Contact us to discuss your project.
