How to Build a Real-Time Image Classification Model on STM32 with CMSIS-DSP

Implementing a Real-Time Image Classification Model on STM32 Using CMSIS-DSP and CMSIS-NN for 32×32 Pixel Grayscale Inputs

This tutorial will guide you through the process of implementing a real-time image classification model on an STM32 microcontroller using the CMSIS-DSP and CMSIS-NN libraries. We will focus on processing 32×32 pixel grayscale images to classify them efficiently.

Prerequisites

• Basic knowledge of embedded systems and STM32 microcontrollers.
• Familiarity with C programming language.
• STM32 development environment set up (e.g., STM32CubeIDE or Keil).
• CMSIS-DSP and CMSIS-NN libraries installed.
• A compatible camera or image source that outputs 32×32 pixel grayscale images.

Parts/Tools

• STM32 Microcontroller (e.g., STM32F4 series).
• Camera module or image source.
• Computer with STM32 development tools installed.
• USB cable for programming.

Steps

1. Set Up Your Development Environment
   1. Download and install STM32CubeIDE.
   2. Set up the CMSIS-DSP and CMSIS-NN libraries by importing them into your project.
   3. Create a new STM32 project in the IDE.
2. Capture and Preprocess Images
   1. Connect your camera module to the STM32.
   2. Write code to capture images and convert them to grayscale.
   3. Resize the images to 32×32 pixels if necessary.

   
           // Example of capturing and preprocessing image
           uint8_t image[32][32];
           captureImage(image); // Your function to capture the image
           convertToGrayscale(image);
           resizeImage(image, 32, 32);
           

3. Load and Prepare the Model
   1. Download or train a model suitable for 32×32 grayscale images.
   2. Convert the model to a format compatible with CMSIS-NN (e.g., TensorFlow Lite).
   3. Integrate the model into your STM32 project.
4. Implement the Classification Logic
   1. Set up the CMSIS-DSP functions for matrix operations.
   2. Prepare the input data in the required format.
   3. Call the CMSIS-NN functions to perform inference.

   
           // Example of classification
           arm_matrix_instance_f32 inputMatrix;
           arm_matrix_instance_f32 outputMatrix;
           arm_matrix_init_f32(&inputMatrix, 32, 32, (float32_t *)image);
           
           // Perform inference using CMSIS-NN
           cmsis_nn_status status = cmsis_nn_model(&inputMatrix, &outputMatrix);
           

5. Run and Test Your Model
   1. Compile the project and upload it to your STM32.
   2. Test the model with various 32×32 grayscale images.
   3. Monitor the output and ensure classifications are accurate.

Troubleshooting

• Issue: Model does not classify correctly.
  • Solution: Check your model training process and ensure the dataset is appropriate.
  • Verify the input image preprocessing steps.
• Issue: STM32 crashes or behaves unexpectedly.
  • Solution: Check for memory overflows, especially when handling images and matrices.
  • Ensure proper initialization of all hardware peripherals.

Conclusion

By following this tutorial, you have successfully implemented a real-time image classification model on an STM32 microcontroller using CMSIS-DSP and CMSIS-NN. You should now be able to capture grayscale images, process them, and classify them efficiently. Experiment with different models and datasets to further enhance your project’s capabilities.