EdgeIR® – AI Ready Infrared Cameras
AI Ready Hardware, for any LightPath Infrared Camera
AI Accelerator, Embedded in the Camera's Video Pipeline
Introducing EdgeIR, the advanced AI accelerator integrated into our camera’s hardware and video pipeline. Powered by the Hailo-8 AI accelerator, boasting 26 Tera-Operations Per Second (TOPS), EdgeIR offers unparalleled performance for real-time data processing. By embedding this powerful accelerator directly into the camera, we eliminate the need for external processing units, significantly reducing latency and enhancing the efficiency of edge computing applications.
Implement your Trained AI Model on the Edge
For customers with trained AI models, our cameras with EdgeIR make deployment seamless. You can implement your neural networks directly within the hardware using standard frameworks such as TensorFlow, Keras, PyTorch, and ONNX. This compatibility ensures a smooth integration process, allowing you to leverage the full potential of your AI models at the edge, optimizing performance for advanced applications like security, industrial monitoring, and smart city projects.
Implement AI Detection with your Thermal Camera
AI detection at the camera level involves embedding artificial intelligence directly within the camera to analyze and interpret visual data in real-time. This method of processing on the edge, as opposed to in the cloud, offers significant advantages. By running AI detection locally, cameras can respond instantly to inputs without the latency associated with data transmission to and from the cloud. This not only enhances the speed and reliability of the response but also improves security by minimizing the amount of data that needs to be sent over the network. Implementing AI detection at the edge ensures efficient, timely, and secure operations for critical applications like surveillance and traffic management.
Object Tracking Using AI
Implementing object tracking directly within the camera’s video pipeline maximizes the potential of edge computing by allowing real-time monitoring and analysis of moving objects. This edge-based processing minimizes latency, as data does not need to be sent to a remote server for analysis, enabling immediate action and decision-making. The localized processing not only reduces bandwidth demands but also ensures data privacy by keeping sensitive visual information within the camera. This is crucial for applications requiring high-security measures and instant data processing, such as in public safety and automated industrial systems.
