Multi-Defect Detection Network for High-Voltage Insulators Based on Adaptive Multi-Attention Fusion
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Published:2023-12-18
Issue:24
Volume:13
Page:13351
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ISSN:2076-3417
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Container-title:Applied Sciences
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language:en
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Short-container-title:Applied Sciences
Author:
Hu Yiming12, Wen Bin12, Ye Yongsheng3, Yang Chao12
Affiliation:
1. Hubei Provincial Engineering Technology Research Center for Power Transmission Line, China Three Gorges University, Yichang 443002, China 2. College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China 3. College of Mechanical and Power Engineering, China Three Gorges University, Yichang 443002, China
Abstract
Insulators find extensive use across diverse facets of power systems, playing a pivotal role in ensuring the security and stability of electrical transmission. Detecting insulators is a fundamental measure to secure the safety and stability of power transmission, with precise insulator positioning being a prerequisite for successful detection. To overcome challenges such as intricate insulator backgrounds, small defect scales, and notable differences in target scales that reduce detection accuracy, we propose the AC-YOLO insulator multi-defect detection network based on adaptive attention fusion. To elaborate, we introduce an adaptive weight distribution multi-head self-attention module designed to concentrate on intricacies in the features, effectively discerning between insulators and various defects. Additionally, an adaptive memory fusion detection head is incorporated to amalgamate multi-scale target features, augmenting the network’s capability to extract insulator defect characteristics. Furthermore, a CBAM attention mechanism is integrated into the backbone network to enhance the detection performance for smaller target defects. Lastly, improvements to the loss function expedite model convergence. This study involved training and evaluation using publicly available datasets for insulator defects. The experimental results reveal that the AC-YOLO model achieves a notable 5.1% enhancement in detection accuracy compared to the baseline. This approach significantly boosts detection precision, diminishes false positive rates, and fulfills real-time insulator localization requirements in power system inspections.
Funder
National Natural Science Foundation of China Hubei Provincial Engineering Technology Research Center for Power Transmission Line (China Three Gorges University) Open Research Fund Project
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
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