Deep learning and artificial intelligence (AI) have revolutionized the industrial and manufacturing world. With AI vision systems, industrial processes are faster, more accurate, and more scalable.
Countless industries have utilized machine vision systems for some years now for applications such as inspection, quality control, and monitoring. While pattern and object recognition were among the first advancements, AI has expanded the technology into newer and more exciting places. Keep reading to learn how AI is revolutionizing vision systems across numerous industries.
What Are AI Vision Systems?
In the past, many hazardous, repetitive, or labor-intensive tasks within industrial and manufacturing environments required human labor to complete. Now, AI-powered machine vision technology has taken its place. These systems use AI technology to inspect, identify, and evaluate products or materials through special cameras and sensors. Then, the computer uses that data to make intelligent decisions.
Consider a food processing facility that makes peanut trail mix. With machine vision, the processing equipment can sort through the peanuts, identify and reject flawed specimens, and ensure that no foreign matter, such as shells or wood chips, makes it into the packaging. As a result, the vision system increases the accuracy and speed of the process, inevitably leading to an increase in productivity and profitability.
Food inspection is only one of the many inspection applications of AI machine vision. Some can even use high-resolution cameras to inspect objects for minute imperfections that would be invisible to the naked eye. In addition, it can perform repetitive tasks — such as counting and measuring — with greater efficiency and increased throughput. Then, the machine can handle those tasks while those workers apply their efforts to more complex jobs.
What Type of Components Are Necessary for Machine Vision?
The components necessary for machine vision vary, but a few standard parts that most (if not all) AI vision systems come with include:
- Lens: Every AI vision system requires a camera lens to view the product or material, take a high-resolution photo, and send it to the computer for processing.
- Lights: For accuracy within machine vision, a light source illuminates the object and makes it visible to the AI and software. Proper lighting is paramount for applications requiring very minute identification or detailed inspections.
- Sensors: Data sensors work together with a capture board and frame grabber to inspect and process images from the camera lens. Then, the machine converts the image into pixels, creating a digital format for easy processing.
- Processor: Every AI vision system follows algorithms and preset business rules. The processor runs the software and any algorithms to process the pixels within the digital image and then uses the data to extract the information it needs, whether it’s object identification, defect detection, or something else.
- Communication: Using an input/output signal or serial connection, the processing system and machine vision cameras communicate with other components within the machine, allowing for a seamless workflow.
The camera is one of the most important parts of machine vision systems, as it gathers the necessary data and information about the product so the AI can analyze it. Generally speaking, AI vision systems use two types of cameras:
Line Scan: A line scan camera is ideal for applications where the items vary in size or move quickly through the production line. The sensor passes over the product in a line when taking the picture and then builds the image pixel by pixel.
Area Scan: This type of machine vision camera is better for taking pictures of objects of the same size in terms of height and width. A rectangular sensor takes photos in a single frame, and the number of pixels in the sensor then matches the height and width of the image.
How Do Machine Vision Systems Use AI?
As many manufacturing plant owners seek end-to-end automation for their production lines, artificial intelligence is paving the way. With autonomous machine vision, the machinery can quickly and accurately identify product defects and use the information it learns to reduce the amount of wasted material and scrap, ultimately improving yield and reducing costs.
For example, AI can better evaluate object texture and shape variations against the product’s expected dimensions than other methods. It can also differentiate between context conditions, such as lighting and deep contrasts, resulting in a higher accuracy rate even under dynamic conditions or when objects are on a highly textured background.
Traditional, non-AI rule-based imaging technology has trouble overcoming those contextual challenges. However, AI can achieve accurate results as effectively as (or even better than) humans. Automated visual inspections also help maintain compliance with ever-changing market conditions, quality standards, work environments, and consumer demands.
In addition, AI is far easier to integrate and adjust than standard rule-based algorithms — no more constant changing of numerical parameters. Instead, workers can obtain the model and provide a data sample to the AI processor. Upgrading is as simple as feeding the computer the updated model. Furthermore, non-engineers can use intuitive software tools and a set of image samples to train neural networks.
With the ability to monitor and correct production issues in real time, AI and machine learning (ML) can reduce costs, improve quality, and ultimately increase profitability for manufacturers.
Deep Learning vs. Traditional Machine Vision
Deep learning and traditional machine vision technologies have some similarities. Manufacturers usually consider the type of application, available processing capabilities, and the amount of data they need to process when determining which technology best suits their needs.
Traditional rule-based technology is better at measuring, gauging, and alignment, whereas deep learning image analysis can handle a variety of tasks, including:
- Assembly verification
- Cosmetic inspection
- Material and texture classification
- Defect idefintication
- Optical character recognition
- Part and feature location
Benefits of Switching to an AI-Powered Vision System
Machine learning and AI allow manufacturers to achieve goals faster than with traditional rule-based vision systems. Common production line goals that AI vision systems help manufacturers meet include:
Reduce Equipment Costs
Adding machine vision to existing equipment or upgrading AI-enabled machinery improves performance. This technology is so advanced and updatable that it reduces the chance of the machinery becoming obsolete.
Lowers Production Costs
AI is highly accurate at detecting defects and flaws, preventing or eliminating the expensive issue of wasted material and low-quality products. It can also identify flaws earlier in the production line, maximizing available work time and reducing the amount of wear and tear on the machinery. Increasing the accuracy when gauging, inspecting, and measuring also reduces the amount of scrap, further lowering production costs.
Increases Floor Space
Manufacturers can process products much faster with machine vision than with manual labor, which would require several workers and a lot of floor space. Instead, a single machine can achieve much faster and more accurate processing rates than humans.
Even when training vision systems to accept business rules and object parameters, the amount of time and complexity level required is nowhere near what is necessary for rule-based learning. Even non-engineers can handle the programming work, making it easier and faster.
Common Applications of ML Vision Systems
AI computer vision systems have a wide range of applications in today’s world of industry and manufacturing. Here are some of the most common applications of machine learning and vision systems:
Object Recognition: Autonomous (self-driving) vehicles depend on AI vision systems to identify the surrounding environment, such as other cars, fallen branches, animals, guardrails, etc. Object recognition is also prevalent in manufacturing and can check label positioning on bottles, boxes, and other packaging types.
Materials Inspection: Machine vision systems have revolutionized the world of quality control. This technology can detect imperfections, flaws, defects, and contaminants in countless products, objects, and materials. One example is checking food for foreign matter or other pollutants, like scraps of metal, strips of paper or packaging, etc.
Optical Character Recognition: Abbreviated as OCR, optical character recognition is an essential application of AI vision systems. The technology allows machines to identify and extract printed information and relay it back to the computer, even handwritten or typed text that is warped or difficult to read.
Signature Recognition: Fraud is an increasingly common crime. With the ability to recognize handwriting and signatures, AI machine vision can use these features to ensure compliance with laws and prevent fraud.
Object Counting: Counting objects is a tedious and time-consuming process that often results in operator fatigue or error. AI vision systems can count objects accurately every time, and their ability to run 24/7 increases productivity and throughput, which gives profits a big boost.
Sciotex: 20 Years of Innovation and Technology
At Sciotex, we know the importance of AI vision systems and deep learning technology. Our team has been developing the technology for over 20 years, helping our clients design and build automated machinery and advanced AI software. As a result, our AI vision systems are unmatched in quality and performance, and our certified engineers continue to innovate every day. Call Sciotex today at (610) 459-9646 to request a consultation or speak with one of our experts.