Computer vision is a technology based on artificial intelligence. It trains systems to extract meaningful information from visual data, including images and videos. CV uses digital images and video from cameras and deep learning models to discover and classify objects and take automated action.
The article explores the most common computer vision applications and their real-world benefits.
5 computer vision applications
Computer vision works by mimicking the abilities of the human brain in recognizing visual information. It uses pattern recognition algorithms to train systems using large visual data sets. Advances in neural network technology allow rapid advancement of these algorithms. For example, retail store automation analyzes images and videos to automate checkout and inventory management.
Let’s discuss the five industries having multiple computer vision applications.
CV plays a crucial role in making the industry safer and more efficient by improving customer experience and revenue generation. From self-driving cars to traffic flow analysis, the Intelligent Transportation System (ITS) has become essential in today’s high-tech world.
Computer vision applications in the transportation industry include.
- Autonomous cars: Autonomous vehicles are the most common computer vision application in the transportation industry. The technology uses object detection algorithms to detect and classify objects in the car’s environment. For example, traffic signs, pedestrians, barriers and other vehicles, to navigate the road safely.
- Analysis of traffic flows: Tracking and estimating traffic flows was made possible using images obtained from drones hovering above an intersection. The technology makes it possible to observe traffic flow and measure traffic variables required by traffic engineers.
Medicine & Health:
Medicine and healthcare have seen great advancements thanks to computer vision technology. Technology is used to improve medical treatments and procedures, accelerate healthcare research, and improve patient experiences.
Here are the two applications of computer vision in the medical and healthcare industry.
- Cancer detection: Computer vision systems are trained using ML and deep learning models with data from healthy and cancerous tissues to accurately detect cancer in patients. A breast cancer study found the technology more effective than human radiologists.
- X-ray analysis: Radiology is one of the first medical disciples to adopt CV-enabled medical apps. Object recognition algorithms combined with CV are used for computer-aided inspection of X-rays, MRIs and CT scans of internal organs. CV allows the detection of bone fractures and the monitoring of treatment results and the detection of changes in radiology.
Public relations and advertisements
Companies in this industry use CV to make better marketing and communications decisions. The technology helps analyze consumer actions and allows marketers to analyze user behavior and response to advertisements.
Two applications of computer vision technology in industry include.
- Monitoring consumer attention and emotions: CV uses facial analysis algorithms to analyze consumers’ facial expressions and identify their emotions and responses to certain advertisements. Therefore, using emotion detection technology in resumes, marketers today predict revenue, measure foot traffic, and set their marketing strategies accordingly.
- Virtual product application: Different shopping brands today are integrating augmented reality and CV to create a virtual shopping experience for customers. For instance, Sephora Virtual Artist is a mobile application that allows you to upload your photo and virtually apply different products to your face to enhance your product selection experience.
- AI-powered image editing: Another computer vision application is AI-powered image editing, where the technology is used to detect objects in an image and separate or modify them from the background. For example, a company known as Removal.AI uses CV technology to function as a background removal tool.
CV helps identify problems and optimize supply chain processes in the manufacturing industry. For example, product and quality inspection and supply chain management.
Two common computer vision applications in manufacturing include.
- Reading texts and barcodes: Since most products have barcodes on their packaging, the industry uses a computer vision technique known as OCR which can automatically detect, verify, convert and translate barcodes into readable text. .
- Product assembly: Computer vision plays a vital role in the assembly of products and components in manufacturing. Industry is implementing computer vision to perform fully automated assembly and product management processes.
Computer vision is used in agriculture to help overcome several challenges including climate change, labor shortage, etc. The technology enables field assessment and detection of crop diseases and helps predict weather conditions and crop yields.
The agricultural industry is increasingly using AI and ML models. This includes computer vision for crop and yield monitoring and product defect detection. With the right mechanical equipment, technology can save time.
Here are the two applications of computer vision in the agriculture industry.
- Crop and yield monitoring: Computer vision is used to monitor plant growth and detect any infectious disease that could destroy the cultivated field. Therefore, this technology has improved yield estimation processes.
- Insect detection: Manual insect detection is inefficient and labor intensive. CV technology uses real-time object detection algorithms for insect detection. It enables vision-based counting and recognition of flying insects that destroy crops.
The future of computer vision applications
Computer vision technology uses deep learning models and AI neural networks to replicate human vision. It provides us with valuable information, improving our quality of life. With new research and refinement of technology, we will see a wider range of functions. Not only will technology become easier to train, but it will also discover more about its surroundings.
Technology has the potential to unlock many new and useful applications. Additionally, CV can be combined with other AI technologies or subsets to create more powerful applications. For example, allowing companies to interact with objects in real time in a multidimensional space.
In addition, CV will help to general artificial intelligence (AGI) and allow it to process information even better than the human visual system.
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