Vision Intelligence: TYQ-i®
You are Industry 4.0 ready. So are we.
TYQ-i - Vision Intelligence for Smart-er Inspection.
Every industry is looking to improve efficiencies - be it early discovery of wear and tear in civil infrastructure or improving finished product quality on an assembly line. TYQ-i® is Ignitarium's Visual Deep Learning based defect detection platform that can vastly improve your inspection and Quality Assurance process.
See - and find - more with TYQ-i®.
Key Features of TYQ-i
TYQ-i Product
Uniqueness
TYQ-i is a distillation of Ignitarium's deep experience in AI-for-vision. At its core, it blends the best of Classical CV techniques with advanced custom neural nets to create an efficient solution that can generalise across vastly different use cases. The same platform can be tuned to detect anomalies in an infrastructural asset spread over miles as easily as it can be re-purposed to detect a defect in a tiny part moving on a high speed conveyer line.
Minimal training images
Handcrafted custom neural networks (not just Open Source CNNs) that can be trained for high accuracy inference with minimal data sets (often less than a few 10s)
Scalable across defect classes
Common Neural Network platform architecture that can detect edge, surface, color, dimensional, missing or extra artifacts
Integrated SDK-based solution
Efficient SDK to allow quick deployment of the solution in live environments, irrespective of defect class being targeted
Works with low-cost cameras
Does not require high-end purpose-built industrial grade Machine-Vision cameras. Can even work with regular webcams in several use cases.
In-built benchmarking frameworks
The TYQ-i framework has a native benchmarking framework that allows quick identification of bottlenecks in the pipeline - be it registration, classification, inference or sub-stages in between.
FPGA-acceleratable architecture
Efficient architecture that can seamlessly transfer critical functions from the Vision processing pipeline into low-cost FPGAs to achieve higher frame rates or reduce latencie
Use cases
TYQ-i
Aerial Analytics
Acquires imagery of infrastructure assets from aircraft (drone & fixed-wing) and performs advanced Visual AI assisted analytics to improve the speed and accuracy of asset monitoring and maintenance.
Aerial Analytics
Use cases :
- Rail track defect detection
- Tower defect detection: Structural analysis of Power transmission towers
- infrastructure mapping
Highlights :
- Defect detection from a distance
- Non-intrusive
- Automatic video capture with perfectly centered ROI
- No manual intervention required by pilot for camera positioning
TYQ-i
Ground Based Infrastructure analytics
Detection of anomalies (wear and tear) in ground infrastructures like railways, roads, tunnels, etc using different types of visual sensors and advanced AI analytics.
Ground Based Infrastructure analytics
Use cases :
- Rail tracks (public transport, mining, etc.)
- Highways
- Tunnels
Highlights :
- Analysis of video and images from 2D & 3D RGB camera sensors
- Multi sensor support (X-ray, thermal, radar, etc.)
- Detection of anomalies in peripheral areas of core infrastructure (Ex: vegetation or stones near rail tracks)
TYQ-i
Real Time Manufacturing Line Inspection
Detection of Edge and Surface deformities in manufactured goods traveling on a conveyer belt
Real Time Manufacturing Line Inspection
Use cases :
- Detection of defects on the surface of manufactured goods (metal, plastic, glass, food, etc.)
- Can be integrated into the overall automated QA infrastructure on an assembly line
Highlights :
- Custom neural network and algorithms to achieve high accuracy and inference speed
- Use of consumer or industrial grade cameras
- Requires only a few hundred images during the training phase
- Supports incremental training of the neural network with data augmentation
- Allows implementation on low cost GPU or CPU based platforms
TYQ-i
Missing Artifact Detection
Detection of presence or absence of Nuts and Bolts on designated slots of machine parts.
Missing Artifact Detection
Use cases :
- Detection of missing components during various stages of manufacturing of industrial parts
- Examples include : missing nuts and bolts, missing ridges, missing grooves on plastic and metal blocks
Highlights :
- Custom neural network and algorithms to achieve high accuracy and inference speed
- Single-pass detection of many categories of missing artifacts
- In-field trainable neural networks with dynamic addition of new artifact categories
- Implementation using low cost cameras and not expensive machine-vision cameras
- Learning via the use of minimal training sets
- Options to implement the neural network on GPU or CPU based systems
Real Time Color Detection
Use cases :
- Machine vision applications such as color sorter or food defect detection
Highlights :
- Color detection algorithm with real time performance
- Detects as close to human vison as possible including color shade discrimination
- GPGPU based algorithm on NVIDIA CUDA and Snapdragon Adreno GPU
- Extremely low latency (a few 10s of milliseconds) for detection
- Portable onto different hardware platforms
TYQ-i in Action
Defect Detection in Railroad
Defect Detection in Telecom Towers
Solar Module Inspection
Defect Detection for the Footwear industry
Defect Detection in Transmission Towers
Edge & Surface : Metal Washers
Edge & Surface : Razor Blades
Missing Artifacts : Nuts & Bolts
Color Detection
TYQ-i Demo Kits
Lattice FPGA-based TYQ-i Demo Kit
Renesas MCU-based TYQ-i Demo Kit
Webinar
Deep Learning-based Vision Analytics For Smart Infrastructure
Webinar Recording
Deep Learning-based Vision Analytics for Wind Turbine Inspection
Webinar Recording