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A Visual Detection Method for Foreign Objects in Power Lines Based on Mask R-CNN

Author

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  • Wenxiang Chen

    (Key Laboratory of Application of Computer Technology of the Yunnan Province, KMUST, China)

  • Yingna Li

    (Key Laboratory of Application of Computer Technology of the Yunnan Province, KMUST, China)

  • Chuan Li

    (Key Laboratory of Application of Computer Technology of the Yunnan Province, KMUST, China)

Abstract

The high-voltage power lines and transmission towers are large in volume, large in number, and wide in coverage, so they are easily attached to foreign objects, which may cause failure of the transmission line. The existing object detection methods are susceptible to weather and environmental factors, and the use of neural networks for target detection can achieve good results. Therefore, this article uses MASK R-CNN as the basic network detection method for detecting foreign objects in the transmission network. The experimental results show that compared with the traditional target detection method, the method adopted in this article has achieved good results in the speed, efficiency, and recognition precision of foreign object detection. In the future, image processing operations can be performed for complex backgrounds of transmission lines to improve recognition effect.

Suggested Citation

  • Wenxiang Chen & Yingna Li & Chuan Li, 2020. "A Visual Detection Method for Foreign Objects in Power Lines Based on Mask R-CNN," International Journal of Ambient Computing and Intelligence (IJACI), IGI Global, vol. 11(1), pages 34-47, January.
    • Handle: RePEc:igg:jaci00:v:11:y:2020:i:1:p:34-47
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    Cited by:

    1. Lijuan Zhao & Chang’an Liu & Zheng Zhang & Hongquan Qu, 2022. "Transmission Line Object Detection Method Based on Label Adaptive Allocation," Mathematics, MDPI, vol. 10(12), pages 1-14, June.
    2. Mohammad Reduanul Haque & Rubaiya Hafiz & Alauddin Al Azad & Yeasir Adnan & Sharmin Akter Mishu & Amina Khatun & Mohammad Shorif Uddin, 2021. "Crime Detection and Criminal Recognition to Intervene in Interpersonal Violence Using Deep Convolutional Neural Network With Transfer Learning," International Journal of Ambient Computing and Intelligence (IJACI), IGI Global, vol. 12(4), pages 154-167, October.
    3. Mohammad Reduanul Haque & Rubaiya Hafiz & Alauddin Al Azad & Yeasir Adnan & Sharmin Akter Mishu & Amina Khatun & Mohammad Shorif Uddin, 2021. "Crime Detection and Criminal Recognition to Intervene in Interpersonal Violence Using Deep Convolutional Neural Network With Transfer Learning," International Journal of Ambient Computing and Intelligence (IJACI), IGI Global, vol. 12(4), pages 1-14, October.

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