Preprocessing takes place through Machine Learning (Supervised Learning). The basic idea is to mimic the way a human inspector would inspect radioscopic images.


Subsequently, a set of geometrical features is extracted from the source as input to a classifier (CNN). Image segmentation is a commonly used technique partitioning an image into multiple segments or regions.


Double Wall Double Image (DWDI) exposure technique is a typical arrangement adopted for taking radiographic images of the pipe with a diameter equal to or less than 80 mm, thereby not allowing any internal access for the insertion of the radiation source.


We make use of pre-trained DNNs to map the knowledge for Visual Recognition. As DNNs are machine learning mechanisms that comprise expanded Convolutional Neural Networks (CNNs or ConvNets), during feature extraction, image classification takes place through CNN or ConVets.


These networks are typically applied to image classification, regression and feature learning, including prediction of series with Deep Long Short-Term Memory Neural Networks.


The CNN layer processes elementary visual features, such as edges and corners, located at different regions of the input. Once the match is made, the results can be viewed on a computer monitor remotely or a mobile device.

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MVP Features

. Desktop/Web Application version
. Administration dashboard settings
. Back up and restoration ability
. File management
. Organization definitions
. Saving the processed image(s) with all inputs & fields in data base for future    reference
.User Management with different access levels to the Luminous application abilities
.Roles definitions for users as per:

       - Admin
       - Supervisor
       - User
       - Clients

.Access permission to users for each page
.User(s) profile definition
.Upload image by client
.image Description
.Processing options:

       - Selecting Color
       - Adding comment and Location…
.Various formats outputs like PDF, CSV and …
. Preparation reports based on all parameters like:
       - Date(s)
       - Description
       - User(s)
       - Damages percentage
       - Type of defect
       - Location

. Optional features selecting while report generation
. Administration dashboard reports with settings ability
. Recently processed images view possibility
. Visibility of processed images and reports along with user/client profile
. Ability of clients feedback after using application on Web Service.


Presently gather Pipeline Welding Maintenance information without human mediation and mistakes, 24 x 7. Save the time your group spends on information assortment by means of an App.

Cost Management

We have utilized the most recent innovation while finding some kind of harmony among cost and components. It is intended to finish the work with less cost, successfully.

Beyond the time barriers

Our App predicts & help you in distinguishing welded joints that need consideration & the region where they need consideration. The development scientific instruments can assist you with doing fast & backing your choices.


Presently access your pipeline's well-being and symptomatic information from anyplace utilizing a cell phone.