39.1. TensorFlowLiteMicro-PersonDetection

39.1.1. Overview

The TFLM example demos the use of a camera to capture an image, perform human detection via the integrated TFLM machine learning platform, and display the results on screen

39.1.2. Configurations

  • Connect the camera device to the CAM interface on the board

  • Click LCD-800480W070TC section and connect

39.1.3. Toolchain Requirements

This example requires a DSP-enabled toolchain to utilize hardware DSP instructions for neural network acceleration:

  • IDE: Segger Embedded Studio (or other IDEs with DSP toolchain support)

  • GCC Toolchain: Andes RISC-V toolchain with DSP extension support or ZCC toolchain with DSP extension support

Note

Standard GCC toolchains without DSP extensions will not be able to compile and run this demo correctly. The DSP instructions are essential for efficient neural network inference.

39.1.4. Code Options

  • Add code before find_package to the project’s CMakeLists.txt to get a higher performance

set(RV_ABI "ilp32d")
set(RV_ARCH "rv32gc")
set(CONFIG_HPM_MATH_NN_RVP32 1)

39.1.5. Running the example

When the example runs successfully, The LCD will display the live screen, the processed image and the recognition result, while the serial terminal will output the score of whether someone is present or not

person score:-23 no person score 23