Build a Smart Meter Scanning Application with Intel's Edge AI Reference Kit
Table of Contents
- Introduction
- The Edge AI Reference Kit
- Running the Smart Meter Scanning Application
- Importing Python Packages
- Preparing the Deep Learning Models
- Defining Essential Parameters
- Loading Models into OpenVINO Runtime
- Data Preprocessing and Post-processing
- Running the Main Function
- Post-processing and Calculating Readings
- Gathering Reading Results
- Running the Overall Application with One Line of Code
- Conclusion
Running a Smart Meter Scanning Application with Intel's Edge AI Reference Kit
In this article, we will explore how to build a smart meter scanning application using Intel's Edge AI reference kit. Smart meter scanning is an essential task in the industrial sector, allowing for accurate and efficient meter reading. By leveraging Intel's AI technology, we can automate this process and greatly improve efficiency.
Introduction
Smart meter scanning is the process of extracting information from industrial meters using computer vision and deep learning techniques. By accurately detecting and segmenting meters within captured images, we can calculate the readings and provide accurate results. In this article, we will guide you through the steps to build and run a smart meter scanning application using Intel's Edge AI reference kit.
The Edge AI Reference Kit
Intel's Edge AI reference kit provides all the necessary materials to build and understand smart meter scanning applications. The kit includes source codes, a readme file, and requirements for better understanding of how the application works. Additionally, a Jupyter notebook is provided to run each function of the smart meter scanning process. By following the instructions in the kit, you can easily build your own smart meter scanning application.
Running the Smart Meter Scanning Application
To begin, we need to import the essential Python packages required for our application. These packages provide the necessary tools and functions needed for the deep learning models and other process steps. Once the packages are imported, we can move forward with preparing the deep learning models for smart meter reading.
Importing Python Packages
We start by importing all the essential Python packages required for our smart meter scanning application. These packages provide the necessary functionalities for our deep learning models and other processes. By including these packages in our code, we can utilize their functions and methods effectively.
Preparing the Deep Learning Models
In order to accurately detect and segment meters within captured images, we need two deep learning models. The first model, PPYolov2, is responsible for meter detection and locating the exact position of meters within an image. The Second model, DeepLabV3P, is responsible for meter segmentation. These pre-trained models are provided by the PaddlePaddle community and can be easily downloaded using the provided URLs.
Defining Essential Parameters
To ensure accurate results, we need to define some essential parameters specific to the type of analog industrial meters being used. These parameters include the Scale interval value, range, and unit of the meters. Additionally, some other required configurations need to be made. By defining these parameters, we can accurately calculate the final meter reading results.
Loading Models into OpenVINO Runtime
Once the models are downloaded, we can load them into our OpenVINO runtime for further inference. This step allows us to utilize the power of Intel's AI technology for efficient and accurate meter scanning. By loading the models into the runtime, we can leverage the optimized performance of OpenVINO for our application.
Data Preprocessing and Post-processing
To prepare the captured image for meter detection, we need to define a preprocessing function. This function prepares the image by applying necessary transformations and filters. Additionally, we need to filter out meter detection results with low confidence and prepare them for the meter segmentation process. We have defined a set of post-processing functions to accurately segment the meters and calculate the final readings.
Running the Main Function
With all the necessary functions defined, we can now run the main function of our smart meter scanning application. This function includes defining the paths for meter segmentation and detection models, as well as visualizing the test image. By executing this step, we can observe the results of the meter detection process, where the exact positions of the industrial meters are located and cropped out for further segmentation.
Post-processing and Calculating Readings
After segmenting the meters using our OpenVINO runtime, we need to post-process the results and calculate the final readings. This involves mapping the circular-shaped intervals and scales to a rectangle shape, locating the exact position of the pointers, and using the defined functions to calculate the meter readings accurately. Through this process, we can obtain precise and reliable meter reading results.
Gathering Reading Results
To provide easy access to the meter reading results, we can print them directly on the screen. Additionally, we have added functionality to print the test image with the meter reading results overlaid. This makes it more convenient to compare the results with the original image and ensures accuracy in the readings.
Running the Overall Application with One Line of Code
For users seeking a simple and efficient way to run the entire smart meter scanning application, we have provided a one-line command that can be executed. By following the instructions in the provided readme file and activating the virtual environment, users can run the application with just this one line of code. The final meter reading results will be printed on the command window, providing a quick and easy solution for users.
Conclusion
In conclusion, building and running a smart meter scanning application using Intel's Edge AI reference kit is a straightforward and efficient process. By leveraging the power of Intel's AI technology, we can accurately detect and segment meters within captured images, resulting in precise and reliable meter reading results. By following the steps outlined in this article, users can easily build their own smart meter scanning applications and streamline meter reading processes in the industrial sector.
Highlights
- Build a smart meter scanning application using Intel's Edge AI reference kit
- Leverage deep learning models for accurate meter detection and segmentation
- Define essential parameters for precise meter reading results
- Utilize OpenVINO runtime for optimized performance and efficient inference
- Print meter reading results on the command window and overlaid on the test image for convenience
FAQ
Q: Can I use this smart meter scanning application for different types of industrial meters?
A: Yes, the application can be customized according to the specific parameters and configurations of different types of industrial meters.
Q: What are the system requirements to run this application?
A: The system requirements include a compatible hardware platform, Intel's AI technology, and the necessary Python packages.
Q: Can the smart meter scanning application be integrated into existing meter reading systems?
A: Yes, the application can be integrated into existing meter reading systems to automate the process and improve efficiency.
Q: How accurate are the meter reading results obtained from the application?
A: The accuracy of the meter reading results depends on the quality of the training data and the performance of the deep learning models used.
Q: Can I contribute or provide feedback to the Intel community regarding this application?
A: Yes, you can join the discussion over on GitHub or the Intel Community Support channel to provide feedback or discuss any questions or concerns.
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