Services
Save Time
Our app gathers pipeline welding maintenance data automatically, 24/7, eliminating human errors and saving your team valuable time.
Save Money
By leveraging cutting-edge technology, we deliver solutions that balance cost and materials, completing work quickly, efficiently, and at lower cost.
Evolving Intelligence
Our AI-powered product gets smarter over time. By learning from Big Data, it continually improves its efficiency and effectiveness as technology evolves.
Smart Detection
Our app highlights welded joints that need attention and shows exactly where issues may occur. Smart tools help you act quickly and make confident decisions.
Mobile Monitoring
Access your pipeline’s health and diagnostic information anytime, anywhere, directly from your mobile device.
AI Technology
Our product harnesses the power of Artificial Intelligence, one of today’s most advanced technologies. AI has revolutionized industries and is driving a new era of innovation.
Luminous Features
System 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
- 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
Input Data Features
- Upload image by client
- Image Description
- Processing options:
- Selecting Color
- Adding comment and Location
Reporting
- Various formats outputs ( PDF, CSV and …)
- Preparation reports based on all parameters:
- 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.
Related Basic Technologies
Pre-Processing
Pre-processing takes place through Machine Learning (Supervised Learning). The basic idea is to mimic the way a human inspector would inspect radioscopic images.
Workflow
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.
DWDI
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.
Pre-Trained DNNs
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 ConvNets.
Classification
These networks are typically applied to image classification, regression and feature learning, including prediction of series with Deep Long Short-Term Memory Neural Networks.
CNN
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.