Industrial Imaging & Machine Vision

Kritikal’s Deep-learning based technology platform provides fast and accurate solutions for AI-based Defect Inspections like Barcode Reading, 360° Inspection, 3D Imaging, Robotic Guidance, and Surface Inspection. Our best-in-class AI-enabled Machine Vision technology with accurate defect classification capabilities requires significantly less training time as compared to rule-based Machine Vision systems and support decision-making on the fly.

KritiKal's Deep-learning based defect Inspection system Features and Benefits

Service Offerings

AI-enabled Barcode Readers and OCR Systems-final
Surface and 360° Defect Inspection
Object Detection and Classification
Robotic Guidance
3D Imaging
Product Development Services

KritiKal's Defect Inspection System

Kritikal’s Deep-learning based defect inspection system contains four modules that allow for Objection detection, localization of key issues in the object, and classification of defects into categories. It also provides an AI-enabled OCR engine that analyses the text in the image and provides a digital output.

Finder

The finder helps in segregating the object from the background and localizing single or multiple features of the image.

Inspector

At this stage, the inspector cross-checks the image for any defect in the product by comparing it with correct images.

Classifier

The classifier analysis the defective items and classifies the defects into categories as per the client's requirement.

Text-Miner

The text-miner is used to extract text data from complex environments like metal surfaces and challenging backgrounds.

Our Methodology

This figure shows how our system works in a real-life scenario. Starting with the problem statement that includes an understanding of defect profile, speed, and accuracy requirements followed by getting images for error classification, testing, and learning purpose to resolve any error before deploying the system online.

KritiKal's defect Inspection system Methodology

Implementation Approach

#1

POC, Proposal Development & Go-Ahead

Sample data is collected from the client and tested on our deep-learning system to get accuracy and defect detection rates as per the client requirements.

This step helps us in proposal development and deciding the implementation approach, Cost, and Project timeline.

#2

Data Gathering &
Ground Truth Preparation

Based on the client’s requirement, camera and lighting systems are implemented at the client site to collect data for our deep-learning algorithm.

In-depth knowledge of the project helps us in deciding the required data and creating a standard for the image to identify all border-line cases.

#3

Acceptance on
Good – Bad Parts

At this stage, the images are analyzed and tested on our Deep-learning based platform to detect & classify good and bad samples.

Continuously training the system against the set parameters and result comparisons help us in optimizing and achieving the best possible accuracy rate.

#4

Defect
Classification

Once the system is ready to classify Good & Bad images, the images get further disaggregated based on defect type classification standards.

Data from 3-5 production lines is sufficient to build and achieve acceptable defect classification rates before deploying the system.

Teams Involved

Teams Involved in KritiKal Defect Inspection project

Community Tools Supported

The AI based Machine Vision practice at KS comprises of experts who have worked on various open source and proprietary business intelligence tools, some of which are outlined below:

AWS

When to Use AI Enabled System

In places where the defect possibility is high Al-based Machine Vision is recommended rather than rule-based MV.

To detect defects that cannot be easily characterized based on quantification, shape, or size.

If defect classification is important for process control and improvement.
e.g. Contamination of grease, dust and chips varying in size, shape, and location.

To detect cracks or cosmetics defects that can come anywhere in the product or inspection of shiny or low contrast items.

To identify defects in components with different background/pattern or challenging feature extraction.

Possible Use Cases

360 Degree Defect inspection System

360-degree Contamination Inspection

Contamination in the Form of Grease, Dust, Hair,
Black Particles, Fly, Chips etc.

Cosmetic Defects Inspection

Cosmetic defect in the form of Scratch, Dent, Burr,
Cracks, Pores, Bubbles, Blisters etc.

Complex Barcode reading

Complex Barcode Reading

Difficult to read Barcode, Shiny surfaces,
Challenging backgrounds etc.

OCR of Complex Surfaces

OCR or Print Inspection on Difficult Surfaces

VIN (vehicle identification) inspection - Deep-learning based OCR in difficult to read background or objects.

Shiny Polished or Transparent Surface Inspection

Engine block inspection, Scratches
inspection on piston rings

Assembly Verification Inspection

Assembly
Verification

Confusing & Challenging backgrounds
where multiple parts are involved.

Case Studies

KritiKal developed a vision system using Aruco Markers that tracks the displacement of washing machine. Find the details here.
An unique ATM Surveillance application that detects the Face and Mask of the person entering the ATM area. Read here.
KritiKal developed a Vision-based truck recognition and classification system that detects and tracks vehicles at construction sites.
RFID & Biometric Based Access Control System that performs multiple security checks to secure the restricted area.
TRAZER Service enables smooth functioning of the toll management system. As it caters different traffic conditions, it delivers 99% accurate traffic data results.

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