In 2020, the global computer vision market generated 9.45 billion USD. From 2021 to 2030, that number is expected to rise to 41.11 billion USD at a compound annual growth rate (CAGR) of 16.0%.
These figures indicate that computer vision (CV) applications are becoming increasingly prevalent across many industries. Faster processing, improved algorithms, and more advanced technology have greatly transformed computer vision since its inception in the mid-twentieth century. Further innovation will only cement CV as a vital tool in the future.
This article will break down the fundamentals of computer vision and how integrating a computer vision platform into tech stacks can change the way companies do business.
What Is Computer Vision?
Computer vision is a field of artificial intelligence that enables computers to see, identify, and describe objects in an environment. A sense of sight for computers, computer vision is a vital tool for many advanced AI functions. These include real-time data collection, predictive analytics, security improvement, and process optimization. All of them allow businesses to maximize operational efficiency and drive huge revenue gains.
Why Is Computer Vision Important?
One way to understand the importance of computer vision is to consider the value human vision brings to the world. Our sense of sight enables us to identify objects, perform tasks, evaluate problems, determine the appropriate course of action in given scenarios, and so much more. Computer vision does the same for tech.
Breakthroughs in artificial intelligence have led to incredible vision system innovations. A computer vision platform can now be trained to complete specific tasks with great precision and efficiency, even outperforming the capabilities of humans.
Just as the brain makes human sight possible, advancements in neural networks enable computer vision systems to learn the way humans do. That means they can gain meaningful insight from digital images and use that knowledge to make data-driven decisions that help companies perform their best.
How Does Computer Vision Work?
As a species, human beings have the most advanced neural capabilities on earth, mainly because of our ability to process information with critical thinking skills. We can take input from our environment through our five senses (one of which is sight) and process that information to identify patterns and solve problems. This ability has enabled us to achieve great progress as a society, and this idea applies to computer vision as well.
Computer scientists have taken cues from neuroscientists to understand how they can replicate human sight in computer vision systems. By understanding how human learning works, computer scientists can give computers a sharper eye.
Computer Vision 101
When humans learn from objects they see, they do it by making inferences about the object from similar images they've previously observed. Their prior experiences with an object identified certain features and established a way to understand that object. So now, when they look at a new image, they classify the image based on the criteria they established before.
A computer vision platform works similarly. For a computer to "see" an object, advanced image-recognition algorithms detect groups of pixels and add labels to specific objects to distinguish them from other items. They repeat this process for thousands if not millions of images and upload the information into a machine learning processor. The processor then draws conclusions about other objects outside its massive database.
Common Computer Vision Techniques and Algorithms
Computer vision is beneficial for many different industries because it has a variety of unique applications. Each of these requires a distinct set of algorithms and data processing techniques, so choosing a computer vision platform like alwaysAI means finding one that can perform your industry's tasks. Here are a few common computer vision techniques:
- Object detection: identifies and labels objects that enter a sensor's field of vision.
- Object tracking: identifies and tracks unique objects throughout a video stream.
- Image classification: distinguishes objects based on unique features that set them apart in their class.
- Pose estimation: locates and predicts the transformation of a human form based on a user-defined reference pose.
- Semantic segmentation: groups image components in the same class to form a coherent idea of the whole.
As we'll see in a moment, these computer vision techniques require multiple technical components working together at once. This includes imaging sensors to capture the data, processors that analyze it and databases that store it. Building a successful CV system requires a cross-functional team of experts in your corner to keep all the moving parts working together seamlessly.
The Complexity of CV
The CV world is rapidly evolving, and developers are creating hundreds of models and frameworks that are uniquely suited to meet a wide variety of industry needs. They are built on complex open-source systems, with varying hardware components — even within brands themselves.
CV framework integration often requires an expert team with a deep understanding of all things tech. A few disciplines that team members must be proficient in are:
- Internet of things (IoT)
- Artificial intelligence (AI)
- Machine learning (ML)
- Edge computing
- Cloud infrastructure
There is more to computer vision than simply devising models and frameworks that can process an image. Creating a quality computer vision platform means forming a development infrastructure that can deliver real-world benefits in unique scenarios. These infrastructure components include:
- A camera or sensor to capture a video stream.
- Models for training and optimization.
- Sophisticated programming for algorithm processing and decision-making logic.
- Connectivity to the cloud for processing or data storage and visualization.
- Deployment on the edge.
Developing a computer vision solution that integrates all of these components is a challenge. However, once companies do, they quickly realize all the benefits CV can bring.
Benefits of a Computer Vision Platform
For some industries, CV will be as essential to their operations as human sight is to ours. Logistics, quality assurance, lower downtime, higher productivity, supply chain dynamics, and greater profits are essential factors that companies must improve daily. Computer vision can help with all of them.
Computer vision is complicated and requires a team of experts to implement, making it expensive and unattainable for many businesses. But there is an easier way - the alwaysAI computer vision platform.
alwaysAI Manages the Complexity
alwaysAI is changing the way people develop and deploy computer vision apps. By hiding the complexity in a single layer, we've streamlined the application process. Our library of Python APIs makes development on the edge easily accessible to any Python developer.
What is edgeIQ?
edgeIQ serves as an intermediate layer between machine learning models and computer vision applications. With the edgeIQ API, you can easily access pre-built Python code for additional computer vision functions like object tracking and facial blurring.
Ease of Deployment
alwaysAI simplifies computer vision deployment by containerizing the model and Python application with no need for the cloud. You can take your packaged computer vision application and deploy it directly onto any number of devices - whether that’s a series of edge devices, a secure server, or a system on-premise.
Users often find the deployment process to be challenging or a major roadblock in their project. alwaysAI offers out-of-the-box functionality to make deployment easier. That's why the alwaysAI computer vision platform is so valuable - we take care of all the tedious tasks for you.
Why Developers Love Us
Streamlining development - alwaysAI speeds up the administration of computer vision and development workflows.
- How do you process images? We handle that automatically.
- How do you resize them or set up a training model - we take care of that.
There's no need to build an entire framework from scratch because we provide pre-trained models in our app, saving you time building custom solutions for your team. Otherwise, you'd have to build some kind of templated structure for your internal team to do these steps and processes themselves.
Pre-Trained Models - alwaysAI’s pre-trained models are ideal for getting started with computer vision. alwaysAI has trained models for a variety of functions for your convenience. In just minutes, you can get started with computer vision. The platform also allows users to upload their own custom-trained models for additional versatility.
Starter Applications - alwaysAI is here to make your life easier. We offer a suite of ready-to-use applications that can be dropped into any project and customized for your specific needs. You can use object detection applications to track unique objects in a retail store. Or use human pose estimation applications to detect when truck drivers are starting to experience fatigue.
When using version control for your codebase, there are many options available. Some of the most popular choices include GitHub and GitLab. The alwaysAI project dashboard helps you integrate any existing hosting service so that all teams can work efficiently and effectively on a single project.
Model Training - alwaysAI provides a complete model training program. Training can be very time-consuming, taking weeks or months to finish. This is true especially if you're starting from scratch, and even if you’re using an existing trained model.
With our no-code toolkit for leveraging premier model architectures like YOLO, we help people train models on data collected from their own devices in just one afternoon. This tool greatly speeds up the data collection and annotation process by automating much of what would otherwise be a very time-consuming process.
What Industries Have Seen Rapid Growth in Computer Vision?
The multitude of computer vision applications makes it useful for nearly any industry. Some of the markets that have capitalized on CV are:
- Transportation - Computer vision is becoming an integral part of transportation systems everywhere. Embedded cameras make automated vehicles possible, and current braking sensors and vision systems are already making our roads safer.
- Retail - Computer vision enables stores to identify low inventory, keep their shelves stocked with high-demand products, and monitor their physical space in real-time to maximize customer experience.
- Manufacturing - Vision systems improve quality assurance because abnormal parts can be identified automatically and removed.
- Construction - The construction industry experiences up to $1 billion in equipment theft each year, and contractors can use computer vision to monitor asset locations and access to job sites.
- Commercial real estate - The real estate industry is one of the most rapidly growing markets for computer vision. Computer vision is the perfect tool to analyze large retail spaces and optimize store layout and consumer behavior.
- QSR - When customers enter a quick-service restaurant, they expect to be served promptly and receive their food as ordered. Computer vision helps QSRs monitor and optimize their service, better-analyzing buying behavior to meet customer demand.
- Inventory - Strained supply chains have left many companies scrambling to keep their shelves stocked. Object counting software enables predictive ordering to fill inventory before a shortage occurs.
Augmented reality, insurance, healthcare, aerospace, and defense are a few other markets leveraging computer vision capabilities to take their businesses to new heights. Will yours?
Examples of Business Applications
Before you integrate a computer vision platform into your operations, you need to know what it is capable of. Businesses can use CV systems to perform many critical activities, such as:
- Retail traffic analysis for optimizing store layouts for efficient flow.
- Predictive maintenance when strained equipment is about to break down.
- Defect detection for better quality assurance.
- A smoother checkout for a better customer experience.
- Real-time customer analytics to give companies a better understanding of their audience.
Many of these business activities are designed to boost productivity and cut costs, but facial and object recognition software can also be part of a company's security strategy, identifying dangers and stopping them before they occur.
Why Should Executives Get Excited About Computer Vision Platforms?
Not only do computer vision platforms have great technical benefits, they also boost profit. Their data-driven intelligence yields high ROI and allows managers and executives to focus on growing their companies. CV leads to higher profits, lower costs, and smarter decision-making. Simply put, computer vision platforms make businesses perform at their best.
At alwaysAI, we provide easy-to-implement CV solutions that have a host of advantages over other platforms or build-it-yourself options. We offer shorter development times, easy model training, industry expertise, and plug-and-play models that suit your application. We can make your business smarter and more efficient.
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