An Improved Mask2Former-HRNet Method for Insulator Defect Detection

Huo, Yaoran; Xiao, Lan; Tang, Zhenyu; Zhou, Jian; Dai, Xu; Xiao, Yuhao; Fang, Xia

doi:10.3390/pr13020316

Open AccessArticle

An Improved Mask2Former-HRNet Method for Insulator Defect Detection

by

Yaoran Huo

^1,*,†,

Lan Xiao

¹,

Zhenyu Tang

¹,

Jian Zhou

²,

Xu Dai

¹,

Yuhao Xiao

¹ and

Xia Fang

³

¹

Information and Communication Company, State Grid Sichuan Electric Power Company, Chengdu 610041, China

²

State Grid Sichuan Electric Power Company, Chengdu 610041, China

³

West China Hospital, Sichuan University, Chengdu 610041, China

^*

Author to whom correspondence should be addressed.

^†

Current address: No.16, Jinhui West Second Street, Wuhou District, Chengdu 610041, China.

Processes 2025, 13(2), 316; https://doi.org/10.3390/pr13020316

Submission received: 25 December 2024 / Revised: 19 January 2025 / Accepted: 23 January 2025 / Published: 23 January 2025

(This article belongs to the Topic Advances in Power Science and Technology, 2nd Edition)

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Abstract

To solve the problem of scale variation in insulator images captured by drones, caused by the lack of control over angle and distance, which makes it hard to detect subtle defects, this paper proposes an instance segmentation method based on an improved Mask2Former-HRNet model for precise localization and defect detection of transmission line insulators. First, a mask-guided and matching component is added to Mask2Former to reduce the misjudgment rate of insulator defects by including noisy label masks. Second, the HRNet backbone network is used to better capture the spatial and shape information of insulators, as it has a stronger feature transfer ability. Deformable convolutions are introduced to handle deformation issues caused by varying angles in insulator images. Then, an attention mechanism is added to focus on key content, improving the network’s attention to crucial information. Finally, experimental results on defect detection of transmission line insulator images captured by drones show that the proposed method increases the detection accuracy by 8.41% and reduces the misjudgment rate by 4.11%. Comparative experiments indicate that the proposed method outperforms existing methods in several evaluation metrics.

Keywords: transmission line inspection; neural network; Mask2former; HRNet; instance segmentation

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MDPI and ACS Style

Huo, Y.; Xiao, L.; Tang, Z.; Zhou, J.; Dai, X.; Xiao, Y.; Fang, X. An Improved Mask2Former-HRNet Method for Insulator Defect Detection. Processes 2025, 13, 316. https://doi.org/10.3390/pr13020316

AMA Style

Huo Y, Xiao L, Tang Z, Zhou J, Dai X, Xiao Y, Fang X. An Improved Mask2Former-HRNet Method for Insulator Defect Detection. Processes. 2025; 13(2):316. https://doi.org/10.3390/pr13020316

Chicago/Turabian Style

Huo, Yaoran, Lan Xiao, Zhenyu Tang, Jian Zhou, Xu Dai, Yuhao Xiao, and Xia Fang. 2025. "An Improved Mask2Former-HRNet Method for Insulator Defect Detection" Processes 13, no. 2: 316. https://doi.org/10.3390/pr13020316

APA Style

Huo, Y., Xiao, L., Tang, Z., Zhou, J., Dai, X., Xiao, Y., & Fang, X. (2025). An Improved Mask2Former-HRNet Method for Insulator Defect Detection. Processes, 13(2), 316. https://doi.org/10.3390/pr13020316

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An Improved Mask2Former-HRNet Method for Insulator Defect Detection

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