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AWS Lookout for Vision

Using Amazon’s Lookout for Vision, it is possible to precisely and comprehensively detect visual flaws in industrial products. It uses computer vision to spot tiny imperfections in silicon wafers—or any other physical object where quality is crucial, like a missing capacitor on printed circuit boards—as well as missing parts in industrial products, damage to vehicles or structures, and irregularities in production lines. Alternatively, it is simply described as Spot Product flaws while using computer vision to detect product defects.

aws lookout

Here are the Key Benefits of AWS Lookout for Vision:

  • Simple to begin
  • Defects can be detected even under unusual conditions
  • Image processing devices that use machine learning (camera)
  • Cost savings and accelerated workflow
  • Constant improvement in accuracy of the product anomaly detection.

AWS Lookout for Vision Detects Deformations as Below:

  •  Scratches on tins, circuit boards, or product dents are easily detected.
  •  Unexpected ballooning on a product.
  • Improper battery cover pasting.
  • Looks for faulty welding, among other things.

Below are Some Samples of the Normal and the Anomaly Ones in Processing Plant:

Vision lookout

Use Cases

  • DAFGARD – Dafgard makes around 100 pizzas per minute of 15 different frozen pizza varieties. Implementing Amazon Lookout has aided in automating and scaling inspection of a variety of product types, including cheese pizza with veggies, by ensuring that cheese is properly covered on a pizza surface and differentiating between variants in it.
  • INVISTA – By automating visual inspections across production lines and providing quick responses to problems, Amazon Lookout for Vision has aided INVISTA, the largest integrated producer of chemical intermediates, polymers, and fibers. This has led to proactive interventions that have increased production efficiency. Additionally, complexity and operating costs have been reduced as a result of eliminating some of the necessary reactionary responses.
  • GE – In manufacturing facilities all over the world, where much of the machinery is not connected or health-monitored, GE a Gas Power supplies and manages power generation equipment. By switching from time-based to predictive and prescriptive maintenance processes, Amazon Lookout for Vision has reduced maintenance expenses and downtime.

Key Features

  • It uses just 30 images to build the model
  • It uses 3 parameters to measure the performance of the model: Precision, Recall, and F1 Score
  • There is no coding involved
  • Amazon takes care of the algorithm and the developer just needs to upload the images,
    and categorize them as Anomaly and Normal

The General Workflow Model

general workflow

Important Aspects to be Followed Before Execution:

  •  Images need to be the same size.
  •  The model’s accuracy is based on the quality of the pictures utilized.
  • There should be 30 images minimum (20 images of Normal and 10 Anomaly images).

The AWS Lookout for Vision Folder Should Contain the Following Folders:

  • Train – Images you can use in a training dataset.
  • Test – Images you can use in a test dataset.
  • Extra Images – Images you can use to run a trial detection.
Conclusion:

Amazon Lookout for Vision, uses AI to scan for potential weaknesses. It cuts costs while quickening work processes because it is a transparent AI service. It thereby enhances output and reduces delay, decreases and prevents faults, minimizes unnecessary downtime as anomalies are discovered right away. Amazon Lookout for Vision is a AI service for detecting possible defects. It reduces cost and expedites the work process.

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