Teledyne FLIR Neuro Technology: Automate Complex Decisions Faster with Deep Learning
Using deep learning methods, developers can quickly automate complex and subjective decision making. The result is the ability to develop systems faster, deliver higher quality products, and enhance productivity.
Normally, deep learning systems require separate cameras and computer systems. Often the images captured for analysis must be sent to a host or cloud system where the neural network provides an inference driven decision. This is often not ideal, relying on remote or cloud-based processing increases latency and introduces reliability and security risks.
Teledyne FLIR Neuro Technology eliminates these risks and simplifies system infrastructure by allowing you to deploy your trained neural network directly to the camera. This reduces system cost and complexity by enabling decisions to be made directly on-camera, in many cases without a host PC. Inference driven decisions occurring on the camera, also called “on the edge”, eliminate system latency and potential security risks. In the case of the Firefly DL camera, both the image capture and the inference decision is made with a system with a 27 mm x 27 mm x 14.5 mm footprint.
Open Platform for Ultimate Flexibility
- To provide maximum flexibility, Neuro supports popular open source frameworks including TensorFlow and Caffe.
- For easy deployment, the NeuroUtility conversion tool for new and experienced deep learning developers helps to deploy classification, detection and localization networks to your Neuro supported cameras – quickly and easily.
Key Deep Learning Functions
Neuro is ideal for the inference functions of object classification, detection and localization such as:
| Classification | |
|---|---|
| Driver/Pilot monitoring | Detecting wakefulness of driver or pilot |
| Production inspection | Classification and sorting of products |
| Fail-safe for biomedical - general | Identification of specific abnormalities in tissues in biopsy samples |
| Missing parts detection | Detecting if any parts which should be included in a box are missing |
| Face recognition - automation | Recognition of faces for building automation |
| Face recognition - security | Recognition of faces for security |
| Solar panel inspection | Differentiation between cosmetic scratches and critical cracks |
| Packaging inspection | Inspection of printed packaging |
| Sign language reading | matching sign language to words |
| Discrete part inspection | Inspecting individual parts |
| Pet detection | Identifying pets to control food dispensing/pet doors |
| PCB inspection | Identifying defects in specific locations PCB |
| Detection and Localization | |
| Textile inspection | Detection of defects in textiles |
| Fail-safe for biomedical - specific defect | Identification of abnormal tissue in biopsy samples |
| Semiconductor wafer inspection - general | Inspection of silicon wafers comparing against known good parts |
| Collision avoidance for UAS | Detection of potential collision hazards for drones |
| Point of sale systems | Identification of products on check-out conveyor belt |
| Detecting out of stock items | Identifying products which are out of stock on a shelf |
| Blister-pack inspection | Inspection of packaging for pharmaceutical products |
| Semiconductor wafer inspection - specific defect | Looking for specific class of defects on a silicon wafer |
| Semiconductor wafer inspection - specific defect | Looking for specific class of defects on a silicon wafer |
| Weed detection | Coordinates passed to weed killing |
| License plate detection | Recognition of printed licence plates |
| Killbot | Guidance for robots which identify and eradicate invasive species |
| Sea lice detection and tracking | Detection and tracking of sea lice on farmed fish |
| Demographic profiling | Estimating age and gender of people in a scene for retail analytics |
| Safety system for mobile robots | Detect people and avoid running them over |
| Soldering inspection | Inspection of solder joint quality |
Functionality to Make Deep Learning Development Easier
- Neuro provides automatic image resizing. Images passed in from the camera are sized to match your neural network parameters
- Neuro provides instant result validation. You can iterate quickly by uploading test images and instantly validate inference results; eliminate the need for a separate test environment. This is achieved with image injection whereby test images bypass the core camera functions and are sent directly to the neural network for validation.
Teledyne FLIR Cameras Supported By Neuro
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