What Is Machine Vision?

Whether you are new to telematics and GPS tracking solutions or already have one, you may have heard the term machine vision (MV) in the mix of options and wondered what is machine vision and how it can help overall fleet operations.

Safety and efficiency are two major concerns for fleet managers as they look for telematics solutions to improve the overall operations of their fleet.

Fleet management telematics solutions use GPS tracking and telematics technologies to give you information about your fleet’s location and activities. Although GPS excels at capturing location data, it has limitations when it comes to monitoring events the way human eyes do. This technology may seem like something out of science fiction, but it does have current real-world applications as a complement to GPS tracking technology.

Definition of Machine Vision

There are wide ranges of definitions for “Machine Vision,” but all of them refer to the methods and technology used to automate the process of getting information from an image. This is not the same as image processing, where the end result is another image. At its core, it covers all non-industrial and industrial applications where a combination of software and hardware gives operational guidance to devices based on digital image capture and processing of those images.

The information captured and extracted can range from basic to complex, depending on what the system requires. It can be used for a range of applications such as automatic inspections, security monitoring, and vehicle guidance. MV comprises many technologies, software and hardware, integrated systems, and actions, methods, and expertise. For fleets, it combines image and video data to analyze actions and make predictions on what may occur due to those actions.

How Do Machine Vision Systems Work

If you are considering a fleet management solution that includes MV, you may wonder what is machine vision technology and how it works. How the system works will depend on the application. It can be used to pick parts or inspect products, which requires the recognition of specific objects (optical character recognition). Or it can be used in fleet management where it will need to identify actions such as distracted driving and events such as speeding.

These applications require systems that work in different ways since they have to see different types of things. However, the process can be simplified into three steps: image capture, lighting, and processing. These systems require image sensors or smart cameras, processing hardware, and software algorithms to automate complex or mundane tasks.

Here’s a quick look at these three steps.

Image Capture

In order to capture images, every MV system requires some kind of sensor or camera that acts as the machine’s eyes. What that looks like depends on the overall goal. A system sensing fill levels at a beverage factory can get by with a simple sensor, while a more complex system will need a more advanced option, such as a camera.

While a sensor or camera acts as the eyes, they don’t see things the way people do. Many sensors see in 2D, and some don’t capture the images in color. Others see even more than the human eye can and capture infrared or ultraviolet wavelengths.

The type of sensor or camera used will depend on the application. The most important things to consider are resolution and sensitivity. Resolution refers to the quality of the image, and sensitivity refers to the lighting conditions. Some machines prioritize resolution while others prioritize sensitivity.

For example, a machine in a factory where the lighting is consistent would prioritize resolution. However, since a fleet vehicle would be driven in various lighting conditions, the system would prioritize light sensitivity. This brings us to the next step in the process.

Image Lighting

Just as people have a hard time seeing in the dark, so does a machine vision system. The better the lighting, the easier it will be for the system to recognize an object. Early versions of machine learning systems needed white light, but today’s system can use various colors.

In fact, the color of the objects the system is required to recognize will dictate the light colors it will need. Using the opposite color of the object generally gives the best results. If the system needs to recognize objects that are one color, a monochromatic light works. However, if the system needs to identify a wider variety of objects or events, they may use multicolor lights or multiple lighting sources. Some machine vision systems can adjust the brightness level when needed.

There are as many lighting options as tasks that you can think of. If the system is set up to capture a silhouette, it may use only a backlight. For other more complex tasks, the system may use front light or light from multiple directions to reduce shadows.

Image Processing

Now that we have lights and image capture, it’s time for action. Image processing action that is. This step is crucial because, without it, MV would just be a fancy camera. This is where artificial intelligence and algorithms analyze the images and govern the response.

In a factory, for example, a machine vision system could be set up to analyze products for defects. Or to look for depth or distance, measuring the objects they “see.” After the algorithm recognizes what they need to the system makes a decision based on the results.

The decision could be as simple and straightforward as an object’s identity or as complicated as alerting a driver of an upcoming adverse event. The greater number of possible responses a machine vision system has, the more advanced its processing is. These systems generally have to make decisions instantly or close to it, no matter how simple or complex the task.

Deep Learning and Machine Vision

More advanced machine learning applications require even more features. A promising and exciting development is the introduction of deep learning. This is a subset of AI that simulates how the human brain works when dealing with unstructured data.

In machine vision systems, deep learning is a part of the image processing step. This step traditionally uses preset rules, but with deep learning, the system can make its own connections and insights. A machine vision system using deep learning can teach itself to be better and recognizing objects and events.

Deep learning also helps these systems be more flexible. For instance, they would be able to understand and recognize product defects they hadn’t previously encountered or read distorted text. Not all MV systems are deep learning-enabled, but the advantages are becoming increasingly popular.

When broken down into its basic parts, machine vision seems relatively straightforward. When you consider how precise each step must be, you get a better understanding of how groundbreaking this technology is.

How Machine Learning Addresses Distracted Drivers and Improves Fleet Safety

Accidents can be expensive and detrimental for fleets. They can cause unexpected delays and damage your fleet’s reputation. With machine vision as part of your telematics solution, you can reduce risky driving behavior in your fleet and improve efficiency as well as the bottom line.

A brief moment of distracted or risky driving can have drastic consequences. The National Highway Traffic Safety Administration (NHTSA) definition of distracted driving includes:

  • Texting or talking on cell phones.
  • Eating and drinking.
  • Talking to people.
  • Changing the volume or channel on the radio

Any activity that takes the driver’s attention away from driving is considered a distraction.

And the distracted driving statistics are terrifying, both for fleet managers and drivers on the road. According to NHTSA, 3,166 people were killed by distracted drivers in 2017. Cell phone use was reported as the cause 14% of the time. In addition, the United States Department of Transportation Federal Motor Carrier Safety Administration (FMCSA) reported that in commercial fleets, the second leading cause for accidents involving fatalities was distracted driving. A separate FMCSA report found that medium and heavy truck collisions cost businesses an average of $200,000 per incident and $3.6 million per fatality.

Benefits of a Machine Vision Camera

Most of the time, commercial fleet drivers get to their destinations safe and sound with no accidents. But it only takes a tiny moment of distraction to increase the risks. Actions that seem trivial – a quick snack or sip of a drink, a brief phone call, or even just changing the channel or the radio increase the risks. This is where MV cameras play a role. Recognizing these small but crucial moments that create risk is the key to reducing and maybe even eliminating road accidents.

Identifying distracted driving moments in the seemingly countless hours that fleet drivers spend on the road and accurately categorizing the behavior as risky is a huge challenge. That’s where machine vision systems come in.

MV cameras can sort through the enormous amount of data generated by the fleet’s onboard video cameras and find the short but key moments that lead to risk by your drivers. Having scan cameras in your fleet vehicles can help prevent expensive accidents and reduce fatalities.

What Is a Machine Vision Camera?

A machine vision camera has special optics that capture images and then process and evaluate them for precise decision-making. It can detect distracted driving and other events such as phone use, fatigue, lane departure, following distance, and more.

With a machine vision camera inside your fleet vehicles, you and your drivers can get alerts when risky driving occurs. MV software instantly alerts the driver if they are showing signs of fatigue or distraction. It also reminds drivers to use their seat belts and stop smoking if they light up while driving.

How Machine Vision Is Used in Other Businesses and Industries

The quality control example above is just one of the ways MV is used in businesses and industries. There are many more ways it helps businesses for identification, inspection, guidance, and more. Below are some industrial machine vision examples.

Inventory Control and Management

If you’ve purchased a product with a bar code, you’ve seen MV in action. This technology plays a significant role in inventory control. In addition, it is also used in manufacturing process to make sure the correct parts and components are added to products as they move down the line. It is also essential for bin-picking tasks robots do in warehouses.


Whether tracking food supplies, heavy equipment on a construction site, or fleet vehicles, machine vision can improve safety.

Product Tracking and Traceability

The pharmaceutical industry and other heavily regulated industries that need to closely track ingredients, serial numbers, and expiration dates, use machine vision to keep track of the entire journey.

Correcting Production Line Defects

MV can identify defects in products and help find where the defect occurs on the production line so that quick corrective steps can be taken.


Harvesting machines use MV to find the location of grapes on the vine so that they can be picked without damaging the plants or destroying any grapes. Machine learning is also used by farm machinery to monitor plants as they grow and detect diseases.

Measurements and Calibration

Whether identifying a gauged that needs to be calibrated or measuring a spark plug to make sure it fits, machine vision can speed up the process and make it more efficient. It can also measure in places where it is difficult or dangerous for people to go.

The Future of Machine Vision in Fleets

Machine learning in fleet management helps alert managers and drivers about risky behavior that can cause accidents. Thanks to these alerts, drivers can get instant feedback and improve their performance. When drivers are on the road, their focus can be split many ways. With a MiX Telematics solutions that include vision learning, fleet managers can give their drivers an extra layer of safety and risk detection to help them avoid accidents.

This is just one of the ways that the technology has enormous potential for fleet management. And it will only continue to improve as more advances in the technology are made.

Learn more about this evolving technology and how it can work for your fleet. Find out MiX Telematics solutions can boost your fleet’s productivity, lower fuel costs, and improve overall fleet safety.


A fully-implemented and supported MiX Telematics solution is guaranteed to improve driver safety and reduce accident rates while also lowering risk, liability and cost.

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