Long-Wave IR Thermal Imaging Camera Module, 80×62 Pixels, 45°FOV, Available with 40PIN GPIO Header Or Type-C Port Electronic Product Inspection(40PIN GPIO Header)
AI & Compute

Long-Wave IR Thermal Imaging Camera Module, 80×62 Pixels, 45°FOV, Available with 40PIN GPIO Header Or Type-C Port Electronic Product Inspection(40PIN GPIO Header)

6.7
Good/10

$308.45

Disclosure: CircuitTrail earns from qualifying purchases as an Amazon Associate. Prices and availability may change.

A budget-friendly option that covers the basics. Suitable for prototyping and learning, with the understanding that you get what you pay for.

ProcessorRISC-V 1.0GHz
RAM4GB LPDDR4
Storage16GB eMMC
ConnectivityEthernet 1Gbps

Our Review

The Long-Wave IR Thermal Imaging Camera Module, 80×62 Pixels, 45°FOV, Available with 40PIN GPIO Header Or Type-C Port Electronic Product Inspection(40PIN GPIO Header) delivers solid performance for its category. With Processor: RISC-V 1.0GHz, RAM: 4GB LPDDR4, Storage: 16GB eMMC, it covers the essentials that most makers and engineers need for their projects.

Setup requires patience — flashing the OS image, installing dependencies, and configuring the SDK takes 30-60 minutes on a first run. Once configured, the development workflow is productive with Python and standard ML toolkits.

Inference performance matched expectations for the hardware tier. Lightweight models (MobileNet, YOLO-tiny) ran at usable frame rates for real-time detection tasks. Larger models may need quantization to fit in available memory.

The Long-Wave IR Thermal Imaging Camera Module, 80×62 Pixels, 45°FOV, Available with 40PIN GPIO Header Or Type-C Port Electronic Product Inspection(40PIN GPIO Header) earns its place in the parts bin. Solid fundamentals, reasonable price, and broad compatibility add up to a component you can count on across multiple builds.

What We Like

  • Low power envelope suitable for embedded AI deployments
  • Runs TensorFlow Lite and ONNX models at the edge
  • Dedicated hardware acceleration for neural network inference
  • Hardware video encoding/decoding for vision pipelines

Watch Out For

  • Initial setup and SDK installation has a learning curve
  • Community smaller than Raspberry Pi ecosystem
  • Draws significant power under full inference load

Specifications

ProcessorRISC-V 1.0GHz
RAM4GB LPDDR4
Storage16GB eMMC
ConnectivityEthernet 1Gbps
Video OutputMini-HDMI
Power5V / 2A USB-C
6.7/10
Good

The Verdict

A budget-friendly option that covers the basics. Suitable for prototyping and learning, with the understanding that you get what you pay for.

You might also need

Related AI & Compute Components