AI Inspection for
Defect Detection
Our computer vision inspection software does a thorough deep scan over the finished surface to find any concealed flaws and notify the concerned professional. Here, a finished product's specific surface is scanned to check for any production-related flaws or damage that may have occurred. The tools at play here that drive surface defect analysis are Artificial Intelligence and deep learning. Our improved AI inspection for manufacturing enables you to fully automate the process of finding and fixing the defects such as surface roughness and flaws on any product.
Pitfalls of traditional approaches of Vision Machines
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Traditional inspection equipment must be often reconfigured to accommodate product changes; however, this apparatus is rigid and challenging to adapt to a fast-paced workplace.
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Because of the limitations of human perception and the variation among manual inspectors, quality control can be unreliable.
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Human inspection error rates are reported to be between 20% and 30%. On the other hand, according to McKinsey study, AI-based visual inspection can enhance productivity by 50% and improve defect detection accuracy by 90%.
Real-world applications of Machine Vision
Product defect detection
Automate the process of finding product flaws (e.g., cosmetic issues, bad welds, assembly errors).
Damage detection
Automate the finding of structure or equipment damage (e.g., surface cracks, water damage).
Corrosion monitoring and detection
Automatically check for corrosion in tanks, vessels, storage piping, boilers, and other machinery.
Equipment inventory management
Transcribing equipment tags rapidly and storing them in a database will help automate asset tagging and management.
Our Process
Acquiring Image Datasets
We first examine the company objectives before building a database of photographs that have been taken from various sources. To act as a benchmark for further comparison, structured, pertinent, and high-quality data are prepared.
Labelling and Processing the Data
Labeling in image processing aids in improving database searchability. The labelled dataset is rigorously evaluated against training data as part of a quality control process.
Data Augmentation
Images are altered using a number of techniques, including as flipping (horizontally or vertically), cropping, blurring, zooming, and compression to improve the training data and train the model for more accurate picture recognition results.
Understanding the Image
The model can accurately comprehend and classify the object indicated in the final stage. The computer programme has now received sufficient training to identify photos from fresh input sources.
Use Cases of AI Machine Vision
Electronic Parts
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Scratches
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Chips
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Cracks
Automobile
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Scratches
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Chips
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Cracks