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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| Vydáno v: | IEEE transactions on instrumentation and measurement Ročník 74; s. 1 - 11 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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IEEE
2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 0018-9456, 1557-9662 |
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| 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 (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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| AbstractList | 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. |
| Author | Wang, Guifang Zhu, Changfei Li, Ying Zhang, Qiang Zhang, Jianing |
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| SubjectTerms | 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) |
| Title | ID-YOLO: A Multimodule Optimized Algorithm for Insulator Defect Detection in Power Transmission Lines |
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