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Volume 18, Issue 3
  • ISSN: 2352-0965
  • E-ISSN: 2352-0973

Abstract

Background

The image processing technology-can be adopted in the power transmission incident detection system, when it is combined with artificial intelligence and machine learning academic knowledge of power transmission surveillance video analysis system, it can automatically detect abnormal objects in the power transmission system. It can quickly and accurately detect abnormal objects, known as environmental object feature detection necessary for the safety of power transmission system.

Objective

In order to improve the object detection ability of the power transmission system, it adopts artificial intelligence and machine learning knowledge in the power transmission surveillance system and it can automatically detect abnormal events or objects.

Methods

Compared with differential binary target detection technique, it proposes a new adaptive background subtraction threshold algorithm to adapt the complex power transmission monitoring environment. Meanwhile, it takes special recognition algorithm for power transmission monitoring environment to ensure the accuracy and stability of the detection system.

Results

The proposed method can be used in the monitoring system and the accuracy and stability of the detection system have been verified through experiments.

Conclusion

Through adopting the special recognition algorithm for power transmission monitoring environment, it can ensure the accuracy and stability of the detection system, in the detection experiments, it can analyze the objects more easily than before.

© 2025 Bentham Science Publishers
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2023-12-08
2025-06-18

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/content/journals/raeeng/10.2174/0123520965266585231106064451
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An Environmental Objects Feature Detection Algorithm based on Image Detection in the Power Transmission System
Recent Advances in Electrical & Electronic Engineering 18, 281 (2025); https://doi.org/10.2174/0123520965266585231106064451
/content/journals/raeeng/10.2174/0123520965266585231106064451
/content/journals/raeeng/10.2174/0123520965266585231106064451
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  • Article Type:     Research Article
Keyword(s): application; Environmental objects; feature detection; image detection algorithm; image processing; power transmission system

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