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A Novel Framework for Automation Technology Based on Machine Vision and Robotics in Electrical Power Inspection
Heng Zhao1, Shengnan Hu2, Qingmiao Chen2
1State Grid Jiangsu Electric Power Co., Ltd. Research Institute, Nanjing 211103, China.
Applied Bionics and Biomechanics
|July 14, 2022
Summary
This study introduces machine vision technology for artifact recognition in electronic systems. It enhances the safety and stability of power systems by improving visual monitoring and device identification.
Area of Science:
- Computer Vision and Electronic Systems Engineering
- Machine Learning for Industrial Applications
Background:
- Traditional manual visual monitoring systems are inadequate for complex electronic and power systems.
- Instability in power systems can arise from various disturbances affecting electrical devices.
- Existing monitoring methods are insufficient, leading to suboptimal issue handling and potential safety concerns.
Purpose of the Study:
- To explore artifact recognition technology powered by machine vision.
- To analyze the composition of machine vision-supported electric power supply recognition systems.
- To propose enhancements for future work in intelligent monitoring systems.
Main Methods:
- Utilizing machine vision technology for data analysis and device identification.
- Applying 'Coach Ken' technology for enhanced perception and machine control.
- Disassembling the compositional methods of machine vision-based power supply recognition.
Main Results:
- Demonstrates the capability of machine vision to identify dominant devices and support shape vision.
- Enables a supervision system integrating perception and machine control.
- Provides a framework for understanding and improving machine vision applications in power systems.
Conclusions:
- Machine vision offers a robust solution for artifact recognition and system monitoring.
- The proposed methods enhance the safety, stability, and efficiency of electronic and power systems.
- This research lays the groundwork for advanced, automated visual inspection in critical infrastructure.
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