Computer vision applications in Transportation and Manufacturing

With AI on the rise, algorithms are getting better and better at visual tasks. Today’s computer vision applications can already read texts with ease. They can identify objects, classify them, and track their movement. They can recognize human faces and convincingly transform them. Moreover, computer vision makes machines comprehend and interpret visual data: from medical imaging to fraud detection, to autonomous driving – the technology is firmly on the way to revolutionize virtually every industry sector.

Consequently, various businesses, whether digitally native or brick-and-mortar, are increasingly utilizing computer vision programs for their operations or exploring novel applications for this technology.

Whether you’re familiar with AI, machine learning and computer vision or new to the concepts, read on. I’ll define computer vision and explore its growth and how it works. Finally, I’ll take you on a tour of computer vision applications being used and refined across five major industries. Almost every sector has use cases for computer vision, but I’ll look at transportation and manufacturing During this exploration, I’ll showcase everyday examples of computer vision applications, illustrating how these technologies are widespread in our daily lives, often without us explicitly recognizing their reliance on computer vision.

Defining computer vision

First, how do they define computer vision? Let’s start with the basics. SImplistically, computer vision technology is the field of computer science that enables computer systems to see and understand the world around them, make decisions about what they see, and act accordingly.

Looking for a more technical definition? Computer vision (CV) is a field of AI that enables computers and systems to derive meaningful information from digital images, videos, and other visual inputs and to take actions or make recommendations based on that information.

What is computer vision vs. machine vision?

There’s a subtle but important distinction between computer vision and machine vision. Computer vision relies on machine learning and uses enormous processing power. Computer vision systems collect as much visual data as possible and then process that information so it can be applied to various tasks. That’s what gives computer vision applications their flexibility.

Machine vision is a lighter-weight subset of computer vision. Machine vision typically focuses on a narrow task. In manufacturing, machine vision (or robot vision) is often used for quality control and to guide objects down an assembly line. I will discuss this further in the section about computer vision and manufacturing.

The goal of computer vision

Computer vision aims to replicate the complexity of human vision. How? By giving computers a way to interpret and understand the world through images. Computer vision applications rely on visual artificial intelligence. The machines are trained on massive datasets of visual information in a process called machine learning. This is the same process used to train other artificial intelligences. The only difference is that the data is in a visual format in computer vision applications.

With enough training, AI software can make sense of visual inputs, but most computer vision technology doesn’t approach human vision. AI still struggles with adaptability, handling ambiguity, and context-based understanding. For example, an early release of Stability’s AI model recognized that a certain element was present on many photos in its training data. Its art generator, Stable Diffusion, started putting that element in photorealistic images. Unfortunately, the AI didn’t have the context to understand what the element really was. It was the Getty Images logo, and using it was an infringement on Getty’s trademark. Stable Diffusion was also telling on itself for training with Getty’s photos without permission.

That said, computer vision technology is impressive and has many use cases. AI is better than humans at some visual tasks and is almost always faster. But before we dive into the use of computer vision in different industries, let’s look at how computer vision technology works today.

How we “see” the world through machine eyes today

Computer vision systems use a combination of hardware and software to extract, analyze, and understand visual information. This information can come from an image or a sequence of images (in other words, a video). In very simple terms, the steps of computer vision include:

1. Machine learning: An algorithm is trained on massive visual datasets
2. Input: Cameras, sensors, and other imaging devices capture visual data
3. Processing: The CV algorithm analyzes the input and identifies patterns, objects, and relationships
4. Decision-making: The machine uses analytics to make informed decisions or predictions
5. Action: The machine performs a task based on its visual analysis and decision-making

Computer vision has been around for decades, but recent developments in artificial intelligence have transformed the processing and decision-making steps. With modern neural network technology, computer vision systems have shot from 50% accuracy to 99% accuracy in less than ten years. That means that in some contexts, computer vision is now comparable to human vision for recognizing and responding to visual input.

Consider these computer vision methods and the complex tasks they make possible:

Recognizing and classifying objects

Computer vision techniques can identify and categorize objects within images with impressive accuracy. This extends to faces, animals, vehicles, specific products, and even complex scenes.

Examples from everyday life include:

• Snapchat — filters can make you look like a cat in a hat because the app recognizes your face
• iPhone Memories — this app personalizes photo collections by classifying your photos into categories

Tracking and detecting motion

Tracking movement and detecting motion are core capabilities of computer vision systems. Motion tracking and detection help machines interpret not just “what” exists in an image but also “when” and “how” a scene is changing.

This dynamic understanding of an image over time unlocks a wide range of applications for computer vision, including:

• Home security cameras — motion-activated sensors can turn on the camera to record suspicious activity
• Autonomous vehicles (AVs) — continuously scanning the environment enables AVs to detect pedestrians, other vehicles, and potential hazards, all while navigating busy streets

Segmenting and analyzing images

Computer vision can be used for breaking images down into their constituent parts. This process, called segmenting, can mean separating foreground from background. It can also involve identifying specific regions of interest. This kind of analysis is crucial for tasks including:

• Automated content moderation — social media companies are using computer vision to detect unwanted content in images or videos automatically

Understanding 3D structure and depth

Computer vision technology isn’t just about flat images. Computer vision systems can perceive depth, grasp objects’ spatial relationships, shapes, and sizes in the real world, and construct 3D models from visual data. Using computer vision in 3D object detection opens doors to applications such as:

• Robotics — understanding the world in 3D helps household vacuum robots navigate complex environments
• Augmented reality (AR) — computer vision apps with accurate depth perception and 3D understanding can create passthrough vision, where virtual objects are seamlessly overlaid onto the real world

Computer vision usage: seeing is believing

While there’s some discrepancy in the exact numbers, research firms agree that computer vision technology is a non-stop growing market. We’ve seen predictions everywhere from an 11% compound annual growth rate (CAGR) over the next ten years to almost 19%.

Global computer vision market. Size by component.

While analysts disagree on the exact numbers, the outlook for computer vision is definitely optimistic. Market.us forecasts the market will grow to $59.8 billion in 2033, as shown in the graph above. Allied Market Research projects that the computer vision market will reach $82.1 billion by 2032.

Their optimism is warranted. With the proliferation of cameras in smartphones, security systems, and other devices, we’re generating more visual data than ever before. This vast data pool serves as fuel for training and improving computer vision projects.

Benefits of computer vision

Advances in deep learning have improved the accuracy and performance of computer vision technologies. Open-source tools and cloud computing platforms’ services have also made the technology more affordable and accessible. As a result, developers and companies of all sizes are building computer vision tools.

Computer vision can be used in systems capable of solving real-world problems all around us:

• Supporting real-time applications by processing visual data much faster than humans can
• Reducing human bias, fatigue, and error by performing menial tasks with consistent results
• Automating and scaling processes that would be unsafe or impractical for humans
• Monitoring environments and equipment to ensure safety and prevent accidents
• Extracting business insights from visual data analytics to support decision-making and strategy planning

Now that you have a solid foundation in computer vision, its growth, and its benefits, let’s dive into use cases. We’ll show how five different industries are using computer vision to transform how they do business.

Computer vision in mobility and transportation

It goes without saying that self-driving cars would not be possible without computer vision. But autonomous vehicle (AV) buyers aren’t the only drivers touched by AI. If you’ve ever used a car with a backup camera that warns you of nearby objects, you’ve encountered computer vision technology. In fact, in recent years, computer vision applications have reshaped everyone’s experience on the road.

Here are just a few examples of computer vision applications in transportation.

Autonomous vehicles rely on four key elements to process images and make real-time driving decisions: car sensors, connectivity, a high-accuracy positioning system, and machine learning algorithms.

Autonomous vehicles use these tools to apply a variety of computer vision techniques in real-time:

• Pattern recognition — for example, to classify objects such as road signs and traffic lights
• Object tracking — to track and respond to moving objects such as other cars or pedestrians
• Segmentation — to identify and select relevant features from raw data, for example focusing on pedestrians despite a busy background
• 3D vision — to understand the relative location of objects and navigate through space

These activities stem from a variety of machine learning algorithms trained for perception and decision-making.

Intelligent tolling systems

Modern toll systems don’t require drivers to stop and pay a fee with exact change. How? They use computer vision to dynamically collect payments, identify violators, and analyze traffic flow.

Intelligent tolling systems can classify vehicles by type to collect the correct toll payment. Plate recognition systems use optical character recognition (OCR) to read license plates from images or video feeds of vehicles. The system can check those numbers against a database of vehicle registrations. It matches the license plate number with the driver’s toll account — or looks up where to send the invoice.

Traffic flow analysis and traffic management

Algorithms can identify and track pedestrians in a scene. It’s important to recognize pedestrians no matter what they’re wearing or how they move. Similarly, traffic cameras count vehicles and monitor the flow of traffic. Computer vision can analyze traffic density on freeways and behavior at urban intersections. All these analytics inform traffic management to reduce traffic jams and improve road safety.

Computer vision in manufacturing

As discussed earlier, machine vision and computer vision methods are both used in manufacturing. As robotic process automation becomes more common in manufacturing processes, it’s getting more sophisticated. Visual intelligence plays a key role in that sophistication.

Predictive and preventative maintenance

Equipment maintenance is critical for worker safety and to minimize downtime. Computer vision can be used to monitor manufacturing equipment for signs of wear and tear. A computer vision system can constantly scan for changes to prevent failures. This is known as predictive maintenance. AI can also identify minor problems and flag them for repair before they cause problems. This is known as preventative maintenance.

Quality control

Quality control is a critical step in manufacturing, but manual inspection is very labor-intensive. In the past, manufacturers used lightweight machine vision systems to automate this process. Now that AI is more accessible, factories are turning to more robust computer vision programs.

Machine vision systems were fussy, requiring specialized cameras and very particular image parameters. Since the new AI systems use machine learning, they are more flexible about input. Computer vision applications can identify parts — and defects — in almost any setting. That means that one computer vision program can function across multiple factories.

If you’re concerned that this flexibility comes with a reduction in accuracy, don’t be. One study analyzed a computer vision algorithm tasked with quality control for brake parts. Defects in these parts are too small to be identified by humans, but the algorithm achieved more than 95% accuracy in detecting them.

Computer vision adoption challenges

While the development of cloud computing and open technologies have made computer vision more accessible, that doesn’t mean it’s easy to get started on your own. The technology is complex and requires a lot of investment and resources. While offering tangible benefits, the implementation of computer vision solutions can exacerbate critical technological challenges such as visual data diversity and integrity, dimensional complexity, data labeling and categorization variability, on top of ethical considerations and cross-organizational readiness.

Many organizations run into multiple problems before robust and efficient systems can be brought to life:

• Complexity and scalability — Machine learning operations (MLOps) require AI and ML expertise, which most organizations don’t have internally
• Cybersecurity — When dealing with huge volumes of data, it’s critical to apply best practices in privacy, security, and compliance
• IoT expertise — Computer vision programs require Internet of Things (IoT) solutions and services which are best entrusted to a knowledgable IoT service provider

Fortunately, companies don’t need to go it alone. Intellias has expertise in all these areas and more. Businesses across industries trust Intellias for the artificial intelligence services, cybersecurity consulting, and IoT solutions they need to make their computer vision dreams a reality. We’ll help you master computer vision while mitigating risks, minimizing losses, and enhancing operational efficiency.

Computer vision is here to stay

Computer scientists have dedicated decades to enabling computers to perceive the world around them, empowering humans to utilize machines to meet their needs. Today, computer vision applications are reshaping our surroundings, yet the technology is just scratching the surface of its potential.

In the foreseeable future, we anticipate that computer vision algorithms will become increasingly robust and pervasive, leading to the emergence of new and potentially unsettling applications.

With Generative AI technology revolutionizing various domains, computer vision is expected to undergo tangible transformations. For example, its ability to generate synthetic data can streamline the training of computer vision systems, such as those used in facial recognition and object detection, making it more cost-effective and less intrusive to privacy. Additionally, it can expedite the labeling of training data, traditionally a laborious and expensive task when performed manually by humans.

The technology for extracting real-time insights from live video has matured, with expectations to expand further. Already deployed in crowd scanning, security surveillance, and factory monitoring, real-time computer vision is poised for valuable new applications as algorithms advance.

By applying computer vision to satellite images, we can monitor diverse activities on Earth, including deforestation, the spread of floods and wildfires, urban expansion, and marine ecosystem dynamics. As satellite imagery and computer vision algorithms advance, we can expect deeper insights facilitating more timely interventions and optimized resource utilization

Furthermore, computer vision is anticipated to comprehend and alleviate risks associated with technology development. Many view computer vision as vital in addressing the threat posed by increasingly convincing AI-generated deepfakes. Its ability to examine images and spot clear signs of algorithmic creation is crucial for distinguishing real from computer-generated content, making it significant in addressing concerns regarding propaganda and detecting disinformation. Issues of bias and fairness permeate all facets of AI but are particularly salient in computer vision. For instance, facial recognition algorithms often demonstrate reduced effectiveness in identifying individuals with darker skin tones, heightening the potential for errors, especially in surveillance or law enforcement contexts. In the years to come, there will likely be a heightened emphasis on privacy-centric AI and computer vision technologies, such as automatic face blurring, designed to operate in public spaces without infringing on privacy rights.

In the fast-changing world of computer vision, partnering with a trusted tech ally can help you navigate innovation and avoid pitfalls, ensuring smooth integration and maximizing benefits.