Machine Vision vs. Computer Vision

When assessing fleet management software for your business, you may have heard the terms machine vision and computer vision. The terms are often used interchangeably and seem to mean the same thing. But do they?

They share some similarities since both of these technologies are involved in helping machines "see" the physical world. However, they often differ in their applications. They both gather and interpret visual data, so it's essential to understand these overlapping technologies.

Although a fairly recent development in fleet management software, the idea of machines being able to see, interpret, and act for us is not new. It's been a part of the science fiction universe for quite some time and is now a part of our reality. Some of the technologies have been developed on different trajectories until they came together into the set of technologies known as machine vision and computer vision.

Which Came First: Machine Vision vs. Computer Vision?

Machine vision was developed first. Although initially created to allow machines to mechanically "see" steps along a production line, its applications have expanded into many other industries. For example, it can detect flaws in products before they are packaged or help ensure food products are labeled correctly.

Introduction to Machine Vision

Machine vision can be considered a simpler form of computer vision. It is fast and lean and usually only requires a programmable logic controller, or PLC-based processing. Machine vision systems capture and process images to output a decision. The system consists of a camera, lens, processor, and software. It can quickly analyze images and make simple automated decisions. These qualities make it useful in manufacturing and other practical applications. It is most often used in inspection, quality control, and guidance.

Introduction to Computer Vision

Computer vision is a set of technologies or digital systems that can process, analyze, and make sense of images the same way humans do. Information is fed into the system, and a computer learns how to process and understand it using special software algorithms. This is similar to machine vision, but there are some nuanced differences.

A computer vision system uses a PC-based processor to dive deep into data analysis. This gives it greater processing capability of acquired visual data compared to machine vision. However, PCs are far more difficult and less robust in a lot of industrial applications and may need to be tailored by software experts.

Another way to think of these technologies is that machine vision is the body of the system, and computer vision is the eye (retina and optic nerve), brain (processing center), and central nervous system. Machine vision uses a camera to capture an image, and computer vision algorithms process and interpret the data before directing other components of the system to take the appropriate action.

Comparing Machine Vision and Computer Vision

The lines between machine vision and computer vision have become increasingly blurred, since both use image capture and analysis to do tasks with accuracy and at a speed that human eyes cannot.

Machine vision is used in industry for autonomous control of machines. It includes computer vision, the technology that makes it possible for images to be processed and understood. A sample use case would be an industrial robot that is specifically equipped and programmed to identify faults in products on the production line. Computer vision is the discipline that develops the algorithms that recognize the visual defect, and machine vision includes the entire system that recognizes the defect and removes them from production.

How Machine Vision and Computer Vision Overlap

Computer vision can be used without being part of a larger machine system. However, a machine vision system doesn't work unless it has a computer and specific software. This goes well beyond simple image processing. The basic components of machine vision and computer vision systems are pretty much the same:

• A camera with an image sensor and a lens
• A frame grabber or image capture board — cameras with a modern interface generally don't need a frame grabber
• A light source suitable to the particular application
• An image processor, either a computer or a smart camera, where the processing takes place inside the camera
• Image processing software

The Biggest Differences between Machine Vision and Computer Vision

In computer vision, an image doesn't have to be a video or photo. It can be data from an infrared or thermal sensor, motion detector, or another source. Advances in computer vision continue to expand its capabilities into processing moving and 3D images, including unpredictable observations that earlier versions of the technology couldn't handle. Complex operations identify many features in an image, analyze them, and offer rich data about them.

A computer vision system is often used to capture, process, and analyze images to acquire a full understanding of them. These systems extract as much information as possible about a scene or an object. A machine vision system zeroes in on the most important parts of an image that pertain to its application. These systems are more often used to make quick decisions.

Machine vision is usually designed with a particular application in mind. It is also generally found in the engineering sphere, while computer vision is generally used in Big Data and the sciences. Simply put, the goal of machine vision is to see and process images and offer useful results based on what they observe. Machine vision uses computer vision in industrial applications, making it a subcategory of computer vision.

Benefits and Application of Each

A quick look at the benefits and applications of machine vision and computer vision can present a better understanding of these two overlapping technologies.

Computer Vision Benefits and Applications

Computer vision quickly and accurately identifies trends and patterns from visual data. Computer vision software can find insights that a human would have a hard time reaching in a timely manner and with as much accuracy. Computer vision has important practical applications in diverse industries, including:

Medical

Detects abnormalities in medical imaging such as x-rays, MRIs, CT scans, or cardiograms.

Insurance

Uses pattern recognition to differentiate intentional damage from accidental damage.

Defense and Security

Automates surveillance to reveal potential criminal activity.

Machine Vision Applications

Machine vision shares some of the same benefits as computer vision in terms of accuracy and speed. The difference is that its applications are focused on managing industrial processes to improve efficiency.

Fleet Management

Machine vision allows fleet managers and drivers to receive alerts when drivers exhibit risky or dangerous behavior. It provides alerts in real-time if drivers exhibit signs of fatigue or distraction. It can also help remind drivers to buckle up.

Automatic Inspection

Assesses products much faster and more accurately than the human eye can, increasing operational efficiency.

Quality Control

Automated quality control is essential for finding flaws in intricate patterns like barcodes that the human eye would have a hard time recognizing. It can also make just about any routine quality check faster, performing pass/fail functions based on the result of the evaluation.

Robot Guidance

Machine vision is a necessary component of many robotic guidance processes. By analyzing visual information about the robot's surroundings, these programs increase speed while allowing for more precise positioning and sorting.

Automotive Industry

On the automotive assembly line, machine vision can inspect and determine if parts have been assembled correctly, ensuring quality and eliminating rework, repair, and scrap. 2D and 3D machine vision can identify these issues.

In addition, machine vision can be used in barcode reading and end-of-line inspection to ensure things like the powertrain and transmission are assembled correctly with no missing parts or extra parts and that all clips are properly placed. Engines and transmissions have a lot of parts with DataMatrix codes (2D barcodes) on them, and these codes are read and several points as part of track and trace procedures.

Food and Beverage

Machine vision systems can inspect bottles as they come down the line to ensure they are filled correctly and that the cap is secure. This reduces waste and ensures the product is filled correctly and safely.

Solar

The solar industry uses this technology to inspect solar panel assembly to ensure that the panels are built correctly and will work at maximum efficiency.

Durable Consumer Goods

Component inspection of consumer goods such as ovens, refrigerators, dishwashers, and microwaves by machine vision can spot whether or not the machines are assembled properly.

Fastener Manufacturing

In fastener manufacturing, machine vision systems can inspect fasteners to make sure they are formed correctly. This helps with quality control and ensures that bad parts don't make their way to the customers.

Plastic Injection Molding

When used in plastic injection molding, this technology can be used to inspect and make sure the parts are fully formed. Deformed or incomplete molded parts can be caused by short shots when not enough plastic material has been injected into the mold. Being able to spot these inconsistencies reduces waste and improves quality.

Process and Steps of Machine Vision

How a machine vision system works depends on the task it is intended to accomplish. However, you can break the general process into three main steps.

1. Image Capturing

The system needs images in order to work. Vision sensors, digital or smart cameras, and infrared or ultraviolet cameras capture images. The hardware captures the image and turns it into digital information.

2. Image Processing

The digital information from the camera can be analyzed with image processing algorithms. The image processing steps are outlined below:

Pre-processing: Contrast enhancing and noise removal.

Image recognition:

Segmentation – the process of applying a threshold and determining the edges of the image.

Feature Extraction – color, size, shape, length, or a combination of these features are extracted during this process.

3. System Action

Using the information extracted during the previous steps, the machine is instructed to perform the needed action.

Machine Vision Cameras

A machine vision camera contains sensors with special optics that capture images, process, evaluate, and measure various characteristics with hardware and computer software to make precise decision-making. If created with the right optics and resolution, machine vision cameras can reveal details almost impossible for the human eye to see.

The main components of a machine vision camera system include lighting, sensor, a communications system, lens, and vision processing systems. The camera's sensor changes the light into a digital image, and it then goes to the processor to be analyzed.

Machine vision cameras are used in many different industries, including fleet management, automotive, packaging, semiconductors, food and beverages, and more. In addition, it has many other applications in location analysis, pattern recognition, and inspection.

AI + Machine Learning

Machine learning is a branch of artificial intelligence (AI) and computer science that focuses on using data and algorithms to imitate the way humans learn and gradually improve their accuracy. If AI enables computers to think in a machine vision system, computer vision lets them observe and understand.

Simply put, AI means using computer systems to do tasks and functions that usually need human intelligence. In other words, it means getting machines to think and act like humans. This is where computer vision comes in. Combined with AI, computer vision automates the type of tasks done by the human eye. In terms of computer vision vs. AI, a good way to think of it is that computer vision is AI applied to the visual world.

How Are Machine Vision and AI Improving Fleet Management?

There are many ways AI and machine learning can help fleet managers, from reducing unplanned downtime to decreasing fuel spend. Machine learning and AI can provide fleet managers with important information that fleet managers can use to optimize operations, and predictive analysis that can help fleet managers make better data-based decisions.

These innovative technologies come together in an AI dashcam that gives fleet managers eyes on their drivers and their drivers' eyes on the road. AI dashcams can detect risky behavior and alert drivers and fleet managers to driver distractions such as phone use, fatigue, smoking, and more. It helps prevent distracted driving, preventing accidents, and keeping drivers safe on the road.