Transfer learning applied to forest fire detection
Image classification on high-perf MCU.
Data collection and annotation is often a tedious and time consuming task to get satisfying image classification results. The transfer learning technique enables to overcome this challenge by reducing the number of images and the training time needed to add a new class. It is applied here to fire detection in the wild, detecting if there is a fire or no fire but can apply to many other situations.
Approach
- The tutorial presents how to use a technique called "Transfer learning" to quickly train a deep learning model to classify images.
- The tutorial is based on the computer vision function pack FP-AI-VISION1
Sensor
Data
Dataset Dataset for forest fire detection (License CC BY 4.0)
Data format
2 classes: fire and no fire
RGB image 250x250x3
Data format
2 classes: fire and no fire
RGB image 250x250x3
Results
Model MobileNetV2 alpha 0.35
Input size: 128x128x3
Memory footprint:
403 KB Flash for weights
225 KBRAM for activations
Accuracy:
Float model: 98%
Quantized model: 98%
Performance on STM32H747 (High-perf) @ 400 MHz
Inference time: 112 ms
Frame rate: 8.9 fps
Input size: 128x128x3
Memory footprint:
403 KB Flash for weights
225 KBRAM for activations
Accuracy:
Float model: 98%
Quantized model: 98%
Performance on STM32H747 (High-perf) @ 400 MHz
Inference time: 112 ms
Frame rate: 8.9 fps
Resources
Optimized with STM32Cube.AI
A free STM32Cube expansion package, X-CUBE-AI allows developers to convert pretrained AI algorithms automatically, such as neural network and machine learning models, into optimized C code for STM32.
Compatible with STM32H7 series
The STM32 family of 32-bit microcontrollers based on the Arm Cortex®-M processor is designed to offer new degrees of freedom to MCU users. It offers products combining very high performance, real-time capabilities, digital signal processing, low-power / low-voltage operation, and connectivity, while maintaining full integration and ease of development.
