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.

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 FLIR 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, FLIR’s 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:  

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.

FLIR Cameras Supported By Neuro

 FLIR Firefly DL camera