ID-YOLO: A Multimodule Optimized Algorithm for Insulator Defect Detection in Power Transmission Lines

Insulators play a crucial role in providing electrical isolation in power transmission lines, and timely detection of their defects is vital to avoid severe human life and property losses. In the context of drone inspections of power transmission lines, accurate and timely detection and localization...

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Bibliographic Details
Published in:IEEE transactions on instrumentation and measurement Vol. 74; pp. 1 - 11
Main Authors: Zhang, Qiang, Zhang, Jianing, Li, Ying, Zhu, Changfei, Wang, Guifang
Format: Journal Article
Language:English
Published: New York IEEE 2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Computer architecture
Convolution
Data integration
Data mining
Defect detection
Defects
Feature extraction
feature fusion
Frames per second
insulator defect (ID)
Insulators
Localization
Location awareness
Modules
Object recognition
Power lines
Power transmission lines
Real time
real-time detection
YOLO
you only look once (YOLO)
ISSN:0018-9456, 1557-9662
Online Access:Get full text
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Summary:Insulators play a crucial role in providing electrical isolation in power transmission lines, and timely detection of their defects is vital to avoid severe human life and property losses. In the context of drone inspections of power transmission lines, accurate and timely detection and localization of insulator defects (IDs) are of paramount importance. Considering the inadequacy of the you only look once (YOLO) series of algorithms in extracting features of insulators and their defects in complex backgrounds, we have designed a method called ID-YOLO to address this challenge. First, we develop the global convolution (GConv) module to integrate spatial and channel information, thereby enhancing the effectiveness of feature extraction. Second, we built the C3-global pooling fusion (C3-GPF) module, aimed at strengthening focus on key data during the feature extraction and fusion stages. Third, we develop the multiscale information fusion (MSIF) module to balance the algorithm's detection accuracy and speed, ensuring superior performance in practical applications. Fourth, we built the weighted feature information fusion (WFIF) module to further enhance the fusion capability of key information. Finally, we adopt the SCYLLA-IoU (SIoU) loss function to replace the original CIoU, thereby improving the algorithm's localization precision and accelerating convergence speed. The experimental results indicate that ID-YOLO achieves an average precision (AP) of 90.9%, representing a 3.3% improvement over the baseline YOLOv5s algorithm. In addition, ID-YOLO achieves a detection speed of 90 frames per second (FPS), meeting the requirements for real-time detection. Practical test results demonstrate that the ID-YOLO algorithm significantly improves detection precision while effectively addressing the challenges associated with multiobject and small-object detection, showcasing its potential application in detecting IDs in power transmission lines.
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ISSN:0018-9456
1557-9662
DOI:10.1109/TIM.2025.3527530