Industrial

Smart cities

Aftermarket wireless digit reader

Equip meters with aftermarket wireless & low-power readers.

Aftermarket wireless digit reader Aftermarket wireless digit reader Aftermarket wireless digit reader Aftermarket wireless digit reader
Aftermarket wireless digit reader Aftermarket wireless digit reader Aftermarket wireless digit reader Aftermarket wireless digit reader

Industrial

Smart cities

STM32Cube.AI

Asset tracking

Vision

Utility companies are responsible for determining the price of water and managing the day-to-day delivery of clean drinking water. Historically, utility operations are tedious and require extensive measures by field personnel. Leveraging automatic meter reading can help utility companies to identify and repair leaks, resulting in the improved performance of the distribution networks. 

Approach

The goal is to automatically read the meter counter thanks to a camera and send the result through low power cellular connectivity. The camera is placed above the water meter. A first neural network detects the Region of Interest, where the digits are located. A second neural network recognizes the digits in a single pass. The recognized number is sent to a server through cellular connectivity LTE Cat M1 or NBIoT.
The demonstration runs on the B-L462E-CELL1 board with the LBAD0ZZ1SE module from Murata which embeds: 
- an STM32L462RE MCU with 512 KB Flash, 160KB RAM, 80 MHz
- an eSIM ST4SIM-200M 
- LTE CatM/NBIoT modem

Sensor

Vision: an Arducam mini 5MP plus camera board is connected to the STM32 through SPI 

Data

Data format
Water meter images with 8 digits
Grayscale image 

Results

Model: Convolutional Neural Network quantized to detect the Region of Interest
Input size: 240x240
Memory footprint:
148 KB Flash for weights
57 KBRAM for activations
Performance on STM32L462 (Low Power) @ 80 MHz
Inference time: 300 ms


Model: Fully Connected and temporal mapper Neural Network quantized to recognize the digits
Input size: 24x140
Memory footprint:
67 KB Flash for weights
66 KBRAM for activations
Performance on STM32L462 (Low Power) @ 80 MHz
Inference time: 900 ms for 8 digits

Optimized with

STM32Cube.AI

Optimized with

Compatible with

STM32L4 series

Compatible with

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.

Optimized with STM32Cube.AI

Compatible with STM32L4 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.

Compatible with STM32L4 series

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