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Published: 12/06/2024

What Is Computer Vision and What Are Its Practical Applications in Different Industries?

Computer vision (CV) technology has tremendously evolved over the decades. What started as simple image scanning evolved into complex systems that can automate tasks, improve security and provide meaningful insights from visual data. Artificial intelligence (AI) has enabled significant growth in the CV field in recent decades, expanding the possibilities across industries.

The Basics of Computer Vision Technology

The current definition of computer vision is a subcategory of AI that allows machines to see and interpret visual data to perform various tasks. It uses machine learning to identify and classify objects and react to them as required. It strives to do what the human brain does automatically: see and process the images to determine the object’s class and distance and to track them if they’re in motion.

How Does Computer Vision Work?

Primarily, CV uses pattern recognition techniques to train itself to understand the visual data it receives. The aim is to mimic the human brain and how it processes visual information. Once completed, the object detection process works similarly to human vision. However, it includes the extra finishing step of providing the visual output.

Computer vision devices are trained on large amounts of data to process images, label the objects present in them, and find ways in those objects. The more training data provided, the more accurate the algorithm is and the better its ability to recognise patterns and classify images will be. Ultimately, the goal is for it to identify the object it’s trained on without fail.

Primary Machine Vision Tasks

 

Object classification: The machine can put the object in a specific category on sight, just like our brain immediately knows that a dog is an animal.

Object Detection: It enables identifying and localising objects within an image by drawing bounding boxes around them. This task involves not only classifying objects but also locating them.

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Object tracking: It allows a machine to follow objects in motion.

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Image segmentation: It allows classifying each pixel in an image to a particular class. Unlike object detection, which provides bounding boxes, image segmentation labels every pixel, distinguishing between different classes within an image.

Disease Detection and Pest Control. Computer Vision for Agriculture

What Is Computer Vision Most Commonly Used for?

Now that you know how computer vision technology works, let’s review its most essential benefits and why it’s become one of the most popular trends in advanced technologies.

Personalised Experiences for Customers and End Users

Computer vision is a powerful tool for creating unique experiences for customers and end users. For example, you can use it to build virtual fitting rooms in clothing stores so customers can try outfits without putting them on. A popular example is applications enabling users to try makeup with just their phone camera.

Improved Efficiency and Accuracy

Artificial intelligence in machine vision systems can significantly increase the efficiency of operations, especially in the industrial sector. Computer vision can be used for inventory tracking and management through visual recognition, accelerating warehouse efficiency and accuracy. It can also be a helpful tool in maintaining high and consistent accuracy. Unlike humans, algorithms and machine learning models don’t get tired and maintain consistent accuracy, minimising the risk of error.

Automation and Autonomous Operations

Computer vision and its object detection capabilities can hugely contribute to automating various tasks, especially repetitive ones. For example, thanks to a vision component, a robot on a farm can recognise and pick strawberries, saving manual labour. Machine vision is also vital to autonomous vehicles, allowing them to track objects autonomously and detect obstacles for seamless and safe movement. Automating specific tasks increases overall efficiency.

Real-Time Data Processing and Analysis

Computer vision systems can process and analyse visual data in real-time, enabling immediate decision-making and action. It’s crucial in dynamic environments where rapid responses are essential, such as traffic management, security surveillance, and autonomous systems.

Recent rapid progress has allowed computer vision applications to grow across many industries. From farming to transportation and Industry 4.0, machines can see and do more than ever. Let’s see what machine vision trends we can expect in selected industries.

Computer Vision in Healthcare

The healthcare industry is one of the first things that comes to mind when discussing image processing. Medical imaging like X-rays, MRI, or CTA scans have been an essential part of computer vision and a game-changer in diagnostics. New advancements in vision systems allow deep-learning algorithms to interpret visual inputs and support diagnostics by indicating anomalies and other points of concern.

Machine vision will also transform surgeries. Experts predict that robotic surgery will become the standard of care for some procedures. The system uses advanced computer vision, among other sensors, allowing surgeons to “feel” what they do during the procedure.

Another trend involves using deep neural networks to help us understand the functions of the human body. Increasingly sophisticated computer vision methods help segment blood vessels, identify stroke aetiology, or detect malignant tumours. Complex radiology assistant tools in development are based on advanced computer vision methods.

By leveraging computer vision techniques, doctors can analyse health and fitness to assist patients in making better medical decisions. In contemporary medicine, doctors use it to gauge blood loss during surgeries, particularly C-sections. When the quantity of lost blood reaches the last stage, it can help take emergency measures more effectively. Additionally, this technology can measure the body fat percentage in people using images captured by regular cameras. The pose analysis algorithm can aid rehabilitation processes and detect postural defects.

Computer Vision in Agriculture

Machine vision and deep learning change how we farm and care for livestock. For example, installing a fixed camera in an animal stall allows the owner to monitor the livestock remotely. Image processing methods like unusual events or mass panic enable automatic anomaly detection. Computer vision can also aid in ensuring proper animal growth. Instance segmentation and pattern recognition methods allow the automatic tracking of individual animals.

A drone equipped with a camera can help a farmer monitor crop growth. Deep learning-based detectors can analyse video data, raise alerts on possible losses, and calculate the waste percentage. Automated monitoring systems can help increase security by automatically detecting intruders or thieves.

Vision systems are also helpful in harvesting. Decision-making systems trained on crop datasets can determine an optimal time window for harvest based on farmland images. A route-planning system with visual aids helps manage the fleet of harvesters, which can save fuel. Inspecting harvests can help detect diseases affecting crops and help find a way to counteract them. Image-supported sorting stands can save manual work by visually inspecting and automatically separating flawed plants.

Applying computer vision technology can automatically sort seeds for maximised yield by discarding potentially flawed sprouts. This process helps select fodder for livestock and alleviate health issues caused by spoilt supplies. A computer vision model can also be used to evaluate the uses of deceased animals.

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Computer Vision in Industry 4.0

Manufacturing and warehousing are another prosperous area for vision systems adoption. Cameras and image recognition software can count and track inventory in real-time, reducing the need for manual checks. Machine vision systems alert staff when stock levels are low, ensuring timely restocking and minimising out-of-stock situations.

Computer vision can also be a valuable tool for automated quality control. High-resolution cameras inspect products for defects or damage, ensuring only quality items are shipped to customers. Vision systems and object recognition can verify that packages are correctly labelled and packed according to standards.

Robots and cobots can use computer vision to navigate complex warehouse environments, avoiding obstacles and optimising routes. Vision systems enable robots to work alongside human workers safely and efficiently, enhancing overall productivity.

Computer Vision in Industry 4.0. Computer Vision Solutions for Manufacturing and Warehousing

Computer Vision in Transportation and Logistics

Machine vision also changes the way we approach the transportation and logistics sectors. Self-driving trucks and delivery vans use computer vision for navigation, obstacle detection, and route optimisation. Advanced Driver Assistance Systems (ADAS) use cameras and sensors to provide lane-keeping assistance, collision avoidance, and adaptive cruise control.

Vision systems are also a great fleet management tool. They enable real-time monitoring of vehicles, tracking their location, speed, and condition to optimise fleet management. Additionally, cameras inside the car can monitor driver behaviour, ensuring compliance with safety standards and reducing the risk of accidents.

Computer vision applications in logistics go beyond autonomous and assisted driving and fleet management. Cameras mounted on vehicles can assess road conditions, identifying potholes, wear and tear, and other maintenance needs. Real-time traffic monitoring helps optimise routes, reduce congestion, and improve overall traffic flow.

Computer Vision for Transporation and Logistics. Fleet Management with Computer Vision

What Are The Most Prominent Challenges of Computer Vision, and How Can You Overcome Them?

Adopting advanced technologies brings challenges, and computer vision is no exception. Here are some of the most common challenges you may face on your journey to implement computer vision and ideas for overcoming them.

1. Running computation-heavy algorithms without the need for a supercomputer

Modern computer vision deals with highly complex tasks that require processing vast amounts of data, which may require a lot of computational power. There are different strategies you can take to overcome this challenge, and they include:

  1. Edge computing: Edge computing involves local data processing on the device where it is generated rather than sending it to a central server. It helps to reduce latency and the need for extensive bandwidth, enabling real-time processing.
  2. Hardware accelerators: Devices equipped with specialised hardware, such as GPUs (Graphics Processing Units) or TPUs (Tensor Processing Units), can perform complex computations more efficiently than traditional CPUs.
  3. Model compression: Techniques such as quantisation, pruning, and knowledge distillation can reduce the size and complexity of deep learning models, making them more suitable for execution on less powerful hardware.
  4. Optimised architectures: Designing neural network architectures specifically optimised for efficiency can significantly reduce computational requirements.

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2. Identifying Unusual Scenarios and Unexpected Situations by Autonomous Vehicles

Autonomous vehicles are a hot topic among the tech enthusiasts. However, we don’t see them on the roads often. There’s still work to identify and react to unpredictable scenarios and situations. Here’s how we can improve it and make driverless cars safer:

  1. Synthetic data generation: Creating synthetic data using simulations and computer graphics can help generate diverse and rare scenarios you shouldn’t encounter frequently in real-world driving.
  2. Data augmentation: Augmenting existing datasets with variations of scenarios can increase the diversity of training data. Techniques include adding noise, changing lighting conditions, and modifying weather conditions in images.
  3. Anomaly detection: Implementing anomaly detection algorithms to identify deviations from typical driving scenarios. These models can flag unusual situations even if they haven’t been specifically trained.
  4. Multi-modal sensing: It combines data from various sensors, such as cameras, LiDAR, radar, and ultrasonic sensors, to obtain a comprehensive understanding of the environment. Each sensor type can help detect different aspects of an unusual scenario.
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3. Object Occlusion

Object occlusion can be challenging when reconstructing a 3D scene from a series of images. Humans have no problem identifying that two visible parts of a dog standing behind a tree are still the same. Unfortunately, this is not the case for computer vision algorithms. Here’s how to aid machine vision in dealing with object occlusion:

  1. Stereo vision: Stereo cameras can capture depth information by comparing images from slightly different perspectives, helping to infer the hidden parts of occluded objects.
  2. Completion network: Training neural networks to predict and complete occluded parts of objects. These networks can learn to infer the missing parts based on contextual clues and prior knowledge.
  3. Point cloud processing: Collecting and processing data from LiDAR or depth sensors can help identify and reconstruct occluded parts.
  4. Manual annotation: Incorporating human annotations to guide the algorithm in identifying occluded objects can be used to improve the training of machine learning models.

4. Dynamic Lighting

Working in dynamic lighting is still a significant challenge for computer vision, as correctly identifying an object in the natural environment is difficult. Here’s how we can address this issue:

  1. Data augmentation: To improve model robustness, augment training datasets with images under various lighting conditions. Techniques include varying brightness and contrast and applying shadows.
  2. Lighting simulations: Use synthetic data generation to simulate different lighting conditions and train models on these varied datasets.
  3. Depth sensing: Integrate depth information from LiDAR or stereo cameras, less sensitive to lighting variations, to aid in object identification.
  4. Real-time adaptation: Implement algorithms that dynamically adjust parameters based on current lighting conditions. It includes adaptive thresholding and real-time histogram adjustments.

Real-World Applications of Computer Vision

Here are some examples of existing computer vision applications transforming industries.

Gaze Estimation Technology for Market Research

Implementing computer vision to create a new way to measure user engagement can benefit the marketing and market research sectors. Gaze estimation tracks the user’s eye movement on a phone screen to see where they are stopping and how much attention they pay to, for example, an ad. It’s a valuable tool for researching and finding new ways to create engaging content for smartphone users.

Stereo Vision and Image Segmentation for the Beauty Industry

Imagine a fully automated manicure robot that can complete a multilevel manicure in minutes without human assistance. Such a robot exists and uses computer vision technologies like stereo vision from multiple cameras to do its job. Thanks to machine learning and object detection, it sees the position of the hand and nails in 3D space to perform precise manicures autonomously.

Object Detection and Tracking in Sports and Physiotherapy

Machine vision is valuable for sports enthusiasts and people undergoing rehabilitation and physiotherapy. We developed mobile applications that help their users perform exercises correctly:

  • One of the applications allows users to use a phone camera to track ball trajectory during a shot on goal, helping football enthusiasts of any advancement level improve their shooting technique.
  • Another app helps those who exercise at home, either for rehabilitation purposes or as part of regular physical activity. It detects the user’s pose during the exercise, indicates the correct angles, and bends for optimal technique to avoid strains and injuries.

User Pose Detection with Computer Vision

Entrust Us with Building Your Computer Vision Solutions

Building advanced computer vision solutions is challenging and requires vast knowledge and expertise. Our engineers can help you create vision systems to change your company’s operations or improve customer and user experience. Contact us to discuss the details.

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