Teledyne Teledyne FLIR Hadron 640R Jetson Nano Test Kit, Dual Thermal & Visible Camera

The kit connects directly to the MIPI CSI-2 camera interface on the Jetson Nano carrier board. Power is supplied via the 5V rail on the Nano's GPIO header or an external 5V source. Install the provided Teledyne FLIR Spinnaker SDK or JetPack's V4L2 drivers to access both thermal and visible video streams. Ensure your Nano is running a compatible Linux kernel version as specified in the Teledyne FLIR documentation for full functionality.

Yes, the radiometric LWIR sensor outputs calibrated temperature data for each pixel. You can access this data via the Teledyne FLIR SDK APIs, which provide functions to extract thermal frames containing temperature values in Celsius or Kelvin. For Python development on Jetson, use the `spinnaker-python` library to capture frames and convert pixel values to temperature using the sensor's calibration parameters embedded in the image metadata.

Development requires the NVIDIA JetPack SDK installed on your Jetson Nano and the Teledyne Teledyne FLIR Spinnaker SDK for camera control and image acquisition. For AI model inference, use frameworks like TensorRT, PyTorch, or TensorFlow. Sample code provided by Teledyne FLIR demonstrates capturing synchronized thermal and visible streams, which can be fed into your neural networks for tasks like object detection or classification using thermal features.

Yes, the kit's hardware and SDK support synchronized capture from both sensors, allowing for pixel-level fusion of thermal and visual data. The Teledyne FLIR Spinnaker SDK provides APIs to trigger both cameras simultaneously, minimizing latency. This enables advanced computer vision techniques like overlaying thermal contours on RGB images or creating multi-modal datasets for training robust machine learning models in applications like autonomous navigation.