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 of insulator defects are of paramount importance. Considering the inadequacy of the YOLO series of algorithms in extracting features of insulators and their defects in complex backgrounds, we have designed a method called Insulator Defect-YOLO (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 Multi-Scale 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. Experimental results indicate that ID-YOLO achieves an average precision of 90.9%, representing a 3.3% improvement over the baseline YOLOv5s algorithm. Additionally, ID-YOLO achieves a detection speed of 90 frames per second, 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 multi-object and small-object detection, showcasing its potential application in detecting insulator defects in power transmission lines.

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Publication Details

Journal
IEEE Transactions on Instrumentation and Measurement
Published
2025-01-01
DOI
https://doi.org/10.1109/tim.2025.3527530
Citations
24
Primary Topic
Advanced Neural Network Applications
Type
article
Field-Weighted Citation Impact
30.62

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article

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

Guifang Wang, Qiang Zhang, Ying Li, Jianing Zhang et al.
24 citations
IEEE Transactions on Instrumentation and Measurement
Advanced Neural Network Applications
30.62
article

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

Guifang Wang, Qiang Zhang, Ying Li, Jianing Zhang, Changfei Zhu
article en
24 citations

Abstract

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 are of paramount importance. Considering the inadequacy of the YOLO series of algorithms in extracting features of insulators and their defects in complex backgrounds, we have designed a method called Insulator Defect-YOLO (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 Multi-Scale 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. Experimental results indicate that ID-YOLO achieves an average precision of 90.9%, representing a 3.3% improvement over the baseline YOLOv5s algorithm. Additionally, ID-YOLO achieves a detection speed of 90 frames per second, 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 multi-object and small-object detection, showcasing its potential application in detecting insulator defects in power transmission lines.

IEEE Transactions on Instrumentation and MeasurementVol. 74
Liaoning Technical University (CN)
National Natural Science Foundation of China
Affordable and clean energy
Openalex Percentile: Top 0%
Advanced Neural Network Applications
30.62
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