Button Text
Home
arrow
Blog
arrow
Computer Vision
Dec 14, 2021
Min Read

AI-based Visual Inspection Automates Product Defect Detection

Share on TwitterShare on Twitter
Share on TwitterShare on Twitter
Share on TwitterShare on Twitter
Share on TwitterShare on Twitter
Matt Parsons
VP of Sales
SUMMARY

Every industry, from microprocessors and smartphones to automobiles and food, has to worry about maintaining quality and output when manufacturing goods. Low or inconsistent quality can lead to additional costs from excess waste, lower yield, product recalls, increased warranty claim rates, and more. As quality issues become more complex, new solutions will be required to rise to the evolving requirements, speed, and unique circumstances that render traditional human-based visual inspection ineffective.

This article explains the advantages of using computer vision powered by artificial intelligence (AI) to automate visual inspection, one of the most common inspection methods for ensuring quality control.  

What is traditional visual inspection?

Visual inspection, or visual testing (VT), is one of the oldest and most basic inspection methods. This common method of quality control does not involve any specialized equipment, instead relying on human senses for verification. This makes visual inspection cost effective as well as easy to perform when compared with other inspection methods. However, inspectors with specialized knowledge are required to achieve reliable quality control, and traditional visual inspection is limited by the speed and sensitivity of human senses.

Pitfalls of traditional approaches to visual inspection

Multiple visual inspections are typically conducted at various points in the typical manufacturing process. Visual inspection is usually a time consuming and error prone process because it is highly manual, requiring intent focus from human laborers over extended periods of time—something most people aren’t particularly good at. Some common problems facing traditional approaches to visual inspection include:

  • Product changes necessitate frequent reconfiguration of traditional inspection machinery, which is inflexible and difficult to adapt in fast-paced environments.
  • Manual inspectors vary based on their experience and the limitations of human perception, which can cause inconsistent quality control.
  • Error rates for human inspectors are estimated at 20-30%. Conversely, McKinsey research states that AI-based visual inspection can lead to a 50% increase in productivity and a 90% improvement in defect detection accuracy.  

What is AI-based visual inspection and why is it better?

AI-based visual inspection involves using machine learning to automatically verify product quality by analyzing unstructured image and video data. AI and computer vision technologies enable manufacturers to automate product defect detection, saving time and money while improving quality control. Some specific benefits that come from augmenting traditional inspection approaches with AI and ML techniques include:  

  • Freeing up mental resources for manual inspectors, improving defect detection.
  • Automatically adapting to changes in products without additional programming.
  • Near instant inspection of tens or hundreds of product areas.

Visual inspection supplemented with AI can also be used for internal and external assessment product facility equipment, such as storage tanks, pressure vessels, piping, and more. AI-based visual inspection leads to more complete and efficient discovery of hidden defects during production. Additionally, no-code solutions for AI application development help manufacturers take advantage of this emerging technology without hiring technical specialists or making significant time and capital expenditures.  

The bottom line for manufacturers is that visual inspection AI surpasses human operators due to its speed, accuracy, and repeatability. A machine vision system can quickly inspect object details that are imperceptible to the human eye with greater reliability and accuracy that people alone. On a production line, AI-based visual inspection can scan hundreds or thousands of parts per minute reliably and repeatedly, far exceeding the capabilities of human workers.

Real-world applications of visual inspection AI

AI-based automation of visual inspection is applied in manufacturing and production for defect detection, product quality assurance, inventory management, and more. Real-world applications of AI-based visual inspection include:

  • Product defect detection: Automate the detection of product defects (e.g., cosmetic issues, bad welds, assembly errors).
  • Damage detection: Automate the detection of equipment or building damage (e.g., surface cracks, water damage).
  • Corrosion monitoring and detection: Automatically monitor corrosion in boilers, pipes, storage tanks, vessels, and other equipment.
  • Equipment inventory management: Automate asset tagging and management by quickly transcribing equipment tags and storing them in a database.

Start automating visual inspection today

Thanks to increasingly accessible deep learning and computer vision tools, it is possible to build intelligent systems capable of exceeding human-level accuracy when it comes to visual inspection and quality control—all without learning to code. Since the system learns and improves as it works, much like people do, deep learning visual inspection merges the adaptability of human assessment with the speed and robustness of a fully automated system.

Identify manufacturing anomalies and other issues that fall outside of production tolerances faster and with greater accuracy than human operators. Build and deploy a custom visual inspection AI solution in a matter of weeks, not months or years.

Additional artificial intelligence resources

Visual inspection AI may not yet be an industry standard however, as adoption rates increase it will soon become one. This powerful solution offers drastic quality control improvements that manufacturers can no longer afford to ignore. For more information, check out the following AI resources:

Other Tags:
Computer Vision
TIC
Share on TwitterShare on Twitter
Share on FacebookShare on Facebook
Share on GithubShare on Github
Share on LinkedinShare on Linkedin

You might also like

close