Revolutionize Energy Management with Smart Meter Scanning

Table of Contents

1. Introduction
2. The Importance of Smart Meter Scanning
3. The Edge AI Reference Kit
4. Running the Smart Meter Scanning Application
5. Preparing the Deep Learning Models
6. Defining Essential Parameters
7. Loading Models into OpenVINO
8. Data Preprocessing and Post-Processing
9. Running the Main Function
10. Post-Processing and Final Results
11. Running the Overall Application with One Line of Code
12. Conclusion

Introduction

In this article, we will explore how to build a smart meter scanning application using Intel's Edge AI reference kit. We will go through the step-by-step process of running the application, from preparing the deep learning models to obtaining the final meter reading results. By the end of this article, you will have a clear understanding of how the application works and be able to build your own.

The Importance of Smart Meter Scanning

Smart meter scanning plays a crucial role in accurately reading and monitoring industrial meters. By leveraging artificial intelligence and edge computing, we can automate the process of meter detection and segmentation, resulting in more efficient and reliable readings. This technology has the potential to revolutionize the way we manage and optimize energy consumption.

The Edge AI Reference Kit

The Edge AI reference kit provided by Intel is a comprehensive Package that includes all the necessary materials and resources for building a smart meter scanning application. It includes source codes, a README file, and the requirements for running the application. The kit also comes with a Jupyter notebook that allows users to run each function of the smart meter scanning process.

Running the Smart Meter Scanning Application

To start running the smart meter scanning application, we first need to import all the essential Python packages. These packages will enable us to perform various functions such as meter detection and segmentation. We also need to prepare the deep learning models required for these tasks.

Preparing the Deep Learning Models

The smart meter scanning application relies on two deep learning models: one for meter detection and another for segmentation. The reference kit provides pre-trained models, PPYolov2 for meter detection and DeepLabV3P for segmentation. By using a few lines of code, we can download these models along with a test image.

Defining Essential Parameters

To ensure accurate meter reading results, we need to define various essential parameters based on the type of analog industrial meters being used. These parameters include the Scale interval value, the range, and the units of the meters. Additionally, there are other configurations that need to be made to optimize the application's performance.

Loading Models into OpenVINO

Once the models are downloaded, we can load them into the OpenVINO runtime for inference. OpenVINO is Intel's software development toolkit for optimizing and deploying AI models on various devices. By utilizing the runtime, we can leverage the power of Intel's AI technology for efficient and fast inference.

Data Preprocessing and Post-Processing

Before running the meter detection model, we need to preprocess the captured image. This involves applying a preprocessing function to enhance the image for better meter detection results. After the detection process, we filter out the results with low confidence and prepare them for the meter segmentation task. This includes defining post-processing functions for accurate segmentation results.

Running the Main Function

The main function of the smart meter scanning application brings together all the previously defined functions and processes. It starts by defining the path for the meter segmentation and detection models. Then, it visualizes the test image, printing it on the screen. The application runs the meter detection model to locate and crop out the industrial meters within the image. Next, it runs the meter segmentation process using OpenVINO, accurately segmenting the scales and pointers of the meters.

Post-Processing and Final Results

Once the meter segmentation is done, we perform post-processing to calculate the final meter reading results. The post-processing functions map the circle-shaped intervals and scales to the rectangular shape, locating the exact position of the pointers. These functions help us calculate the reading results accurately. The results are displayed on the screen and also printed on the test image for easier comparison.

Running the Overall Application with One Line of Code

To simplify the process of running the smart meter scanning application, the reference kit provides PY source code files. By following the instructions in the readme file and activating the virtual environment, we can run the application with just one line of code. This command executes the PY source code files and displays the final meter reading results on the command window.

Conclusion

Building a smart meter scanning application is now more accessible than ever, thanks to Intel's Edge AI reference kit. By following the steps outlined in this article, you can create your own application for accurately reading and monitoring industrial meters. The combination of artificial intelligence and edge computing opens up new possibilities in optimizing energy consumption and improving efficiency.

Pros

• Automated and accurate meter reading
• Efficient energy consumption management
• Easy integration with Intel's Edge AI reference kit
• Simplified running process with one line of code

Cons

• Reliance on specific deep learning models
• Configuration and parameters customization required for different meter types

Highlights

• Building a smart meter scanning application with Intel's Edge AI reference kit
• Leveraging artificial intelligence for accurate meter detection and segmentation
• Preparing and loading deep learning models into OpenVINO
• Data preprocessing and post-processing for precise results
• Running the application with one line of code

FAQs

Q: Can I use any deep learning models for meter detection and segmentation? A: The reference kit provides specific pre-trained models (PPYolov2 and DeepLabV3P) for meter detection and segmentation. These models have been optimized for this application.

Q: Is it possible to customize the application for different types of industrial meters? A: Yes, the application allows for the customization of essential parameters such as scale intervals, ranges, and units. This flexibility enables the adaptation of the application to different types of analog industrial meters.

Q: What are the system requirements for running the smart meter scanning application? A: The system requirements are detailed in the readme file provided in the reference kit. They include the necessary software dependencies, hardware specifications, and instructions for setting up the virtual environment.

Resources

• Intel's Edge AI reference kit
• OpenVINO toolkit