SofTeCode Blogs

One Place for all Tech News and Support

The value of AI-based in visual Inspection

4 min read

image credit read write


For over a decade Ai-based Models are being used, manufacturers have turned to automated solutions to enhance their bottom line. Automation and machine vision are now being augmented and even replaced by AI. Here is the value of AI-based visual inspection in 2020.

Value of AI-Based visual inspection

Being replaced by AI is particularly true when it involves visual inspection. the utilization of AI-based visual inspection technology is transforming manufacturing’s ability to enhance business operations.

AI-based visual inspection relies on two of AI’s main strengths: computer vision and deep learning. Every AI system is made with the core capacity to perceive its environment (computer vision) and act on those perceptions (deep-learning).

As a result of deep-learning, AI adapts to a variety of environments, making it useful across a mess of industries. it’s unlimited potential and maybe developed rapidly to satisfy a manufacturer’s needs.

Concept of AI-based visual inspection

Well-trained human eyes can detect defects. A well-trained AI-based vision system can do an equivalent — but with greater efficiency. sort of a human eye, AI-based vision systems capture a picture and send it to a central “brain” for processing.

Like a human brain, an AI “brain” makes detailed meaning from the image by contrasting it against its existing knowledge.
AI-based vision systems are made from two integrated components. A sensing device acts as an “eye,” while a deep learning algorithm acts as a “brain.” The integrated system successfully mimics the human eye-brain ability to interpret images.

AI-based vision systems are more efficient than human eyes because the AI “brain” stores greater amounts of data.

Robust computational power can parse through available data at rapid speeds. The system can classify objects in both photos and videos and perform complex beholding tasks.

AI-based vision systems can search for images and captions, detect objects, and classify multi-media.

Thanks to deep learning-based visual processing, AI-based visual inspection systems can perceive cosmetic flaws and detect defects across general or conceptual surfaces (MobiDev dot biz).

Benefits of AI-based visual inspection

1. Fast Implementation
Decades-old automated systems depend upon defect libraries, lists of exceptions and sophisticated filters. The time it takes to accrue this information, clean it for accuracy, and re-implement it decreases its efficacy. It also wastes labor.

AI and deep learning don’t require prolonged programming or tediously lengthy algorithms. AI-based visual inspection systems could be constructed by several quality engineers and a dataset of coaching images. The system learns rapidly and is integrated over several weeks.

2. Improved Analytics and internal control
Manufacturers can use AI to document inspection results and to assess product quality. Some overall process improvement initiative metrics which will be successfully tracked and correlated with concrete vision data include:

  • process recipes
  • equipment differences
  • component suppliers
  • factory location
  • In addition, inspection images and results also can be tracked and documented. These initiatives prevent future failure, which saves time and extra production costs. Applying deep learning-based machine vision across all initiatives and inspections helps manufacturers recognize and address defects early.

3. Labor Costs Reduction
AI solutions have higher rates of consistency than most expert human inspectors. Human inspectors must be trained and are only ready to maintain a high degree of focus for 15-20 minutes at a time. Labor costs are incurred yearly and staff turn-over is a problem. For these reasons, AI-based vision inspections are less expensive than manual labour.

Use Cases

AI is increasing the competitiveness of manufacturers across every industry. Here are recent use cases from the aviation industry, semi-conductor manufacturing sector, and bio-science.

Alibaba has risen to satisfy healthcare challenges created by the coronavirus. Alibaba’s deep-learning-based visual recognition system is capable of detecting the coronavirus in chest CT scans at a 96% accuracy rate. The system accessed 5,000 COVID-19 cases and may provide a diagnosis within 20 seconds. Moreover, the system can differentiate between images of viral infection and pictures of coronavirus.

Fujitsu Laboratories implemented a picture Recognition System at Fujitsu’s Oyama factory. The system ensures that parts are produced at optimal quality levels by supervising the assembly process. The system was so successful that Fujitsu implemented it across everything of the company’s production sites.

Airbus introduced an automatic, drone-based aircraft inspection system in 2018. The system has improved the standard of inspections and reduced aircraft downtime.

GlobalFoundries may be a leader in semiconductor manufacturing. the corporate designed a visible inspection system that detects defects during a scanning microscope (SEM) images. The system detects defects during a wafer map which then helps to work out the semiconductor device’s performance.

The use cases listed above reveal the extent to which AI is capable of automating many aspects of our lives. Although AI vision will never replicate human vision, the technology continues to classify information and advance in ways human eyes and brains cannot. And only humans might consider the way to use this technology to urge advantages.


source: read write

Read More:

How to make Good Newsletter using AI

Grow your skills and job opportunity with Google Cloud

What kind of Data is used in stock marketplace?

How useful was this post?

Click on a star to rate it!

Average rating 0 / 5. Vote count: 0

No votes so far! Be the first to rate this post.

Give your views

This site uses Akismet to reduce spam. Learn how your comment data is processed.