LDDFSF-YOLO11: A Lightweight Insulator Defect Detection Method Focusing on Small-Sized Features
Insulators are essential for maintaining electrical isolation in transmission lines, with the timely and accurate identification of their defects via uncrewed aerial vehicle (UAV) being crucial for ensuring operational stability and safety in high-voltage transmission lines. Due to complex environme...
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/11003913/ |
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| author | Peng Shen Keyu Mei Huiqiong Cao Yongxiang Zhao Guoqing Zhang |
| author_facet | Peng Shen Keyu Mei Huiqiong Cao Yongxiang Zhao Guoqing Zhang |
| author_sort | Peng Shen |
| collection | DOAJ |
| description | Insulators are essential for maintaining electrical isolation in transmission lines, with the timely and accurate identification of their defects via uncrewed aerial vehicle (UAV) being crucial for ensuring operational stability and safety in high-voltage transmission lines. Due to complex environmental conditions, varying viewing angles, and unstable lighting conditions, existing insulator defect detection algorithms still face challenges, including missed detection, false detection, and inadequate adaptability for detecting small-sized defects in UAV captured images. This study proposes a lightweight insulator defect detection method (LDDFSF-YOLO11) that focuses on small-sized features to efficiently detect and identify insulator defects. In the LDDFSF-YOLO11 model, a focused feature extraction module (MFFEConv) is proposed to enhance the feature extraction capability of the backbone network. In addition, a collaborative attention mechanism and a multi-scale feature fusion module (MDMF) are proposed to enhance the model’s ability to detect small-sized defects in remote and complex backgrounds, effectively solving the problems of missed detections, false detections, and poor adaptability to complex backgrounds. In addition, we propose an RWLoss loss function to improve the accuracy of bounding box matching in the model. And optimize the network structure through channel pruning technology to improve model lightweighting. Finally, experimental validation was conducted using a self built insulator dataset and the China Power Line Insulator Dataset (CPLID). The experimental results showed that compared with the baseline model YOLO11, the LDDFSF-YOLO11 model improved AP50 by 9.8%, 8.2%, and 5.4%, mAP by 7.1%, and reduced model size by 56.1%, accounting for only 4.2MB, in three types of small-sized defects: fractional insulator, drop insulator, and flash insulator, respectively. The LDDFSF-YOLO11 model can quickly and accurately identify small-scale defects in insulators in complex backgrounds, making important contributions to the safe and stable operation of high-voltage transmission lines. |
| format | Article |
| id | doaj-art-ae412ed920e04da3a2eec106453a12c1 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-ae412ed920e04da3a2eec106453a12c12025-08-20T02:16:49ZengIEEEIEEE Access2169-35362025-01-0113902739029210.1109/ACCESS.2025.356997011003913LDDFSF-YOLO11: A Lightweight Insulator Defect Detection Method Focusing on Small-Sized FeaturesPeng Shen0Keyu Mei1https://orcid.org/0009-0004-4436-1374Huiqiong Cao2Yongxiang Zhao3Guoqing Zhang4https://orcid.org/0000-0003-2169-9060North China Institute of Aerospace Engineering, Langfang, ChinaNorth China Institute of Aerospace Engineering, Langfang, ChinaNorth China Institute of Aerospace Engineering, Langfang, ChinaNorth China Institute of Aerospace Engineering, Langfang, ChinaNorth China Institute of Aerospace Engineering, Langfang, ChinaInsulators are essential for maintaining electrical isolation in transmission lines, with the timely and accurate identification of their defects via uncrewed aerial vehicle (UAV) being crucial for ensuring operational stability and safety in high-voltage transmission lines. Due to complex environmental conditions, varying viewing angles, and unstable lighting conditions, existing insulator defect detection algorithms still face challenges, including missed detection, false detection, and inadequate adaptability for detecting small-sized defects in UAV captured images. This study proposes a lightweight insulator defect detection method (LDDFSF-YOLO11) that focuses on small-sized features to efficiently detect and identify insulator defects. In the LDDFSF-YOLO11 model, a focused feature extraction module (MFFEConv) is proposed to enhance the feature extraction capability of the backbone network. In addition, a collaborative attention mechanism and a multi-scale feature fusion module (MDMF) are proposed to enhance the model’s ability to detect small-sized defects in remote and complex backgrounds, effectively solving the problems of missed detections, false detections, and poor adaptability to complex backgrounds. In addition, we propose an RWLoss loss function to improve the accuracy of bounding box matching in the model. And optimize the network structure through channel pruning technology to improve model lightweighting. Finally, experimental validation was conducted using a self built insulator dataset and the China Power Line Insulator Dataset (CPLID). The experimental results showed that compared with the baseline model YOLO11, the LDDFSF-YOLO11 model improved AP50 by 9.8%, 8.2%, and 5.4%, mAP by 7.1%, and reduced model size by 56.1%, accounting for only 4.2MB, in three types of small-sized defects: fractional insulator, drop insulator, and flash insulator, respectively. The LDDFSF-YOLO11 model can quickly and accurately identify small-scale defects in insulators in complex backgrounds, making important contributions to the safe and stable operation of high-voltage transmission lines.https://ieeexplore.ieee.org/document/11003913/Uncrewed aerial vehicle inspectiondefect detection of insulatorssmall size featuresYOLO11lightweight model |
| spellingShingle | Peng Shen Keyu Mei Huiqiong Cao Yongxiang Zhao Guoqing Zhang LDDFSF-YOLO11: A Lightweight Insulator Defect Detection Method Focusing on Small-Sized Features IEEE Access Uncrewed aerial vehicle inspection defect detection of insulators small size features YOLO11 lightweight model |
| title | LDDFSF-YOLO11: A Lightweight Insulator Defect Detection Method Focusing on Small-Sized Features |
| title_full | LDDFSF-YOLO11: A Lightweight Insulator Defect Detection Method Focusing on Small-Sized Features |
| title_fullStr | LDDFSF-YOLO11: A Lightweight Insulator Defect Detection Method Focusing on Small-Sized Features |
| title_full_unstemmed | LDDFSF-YOLO11: A Lightweight Insulator Defect Detection Method Focusing on Small-Sized Features |
| title_short | LDDFSF-YOLO11: A Lightweight Insulator Defect Detection Method Focusing on Small-Sized Features |
| title_sort | lddfsf yolo11 a lightweight insulator defect detection method focusing on small sized features |
| topic | Uncrewed aerial vehicle inspection defect detection of insulators small size features YOLO11 lightweight model |
| url | https://ieeexplore.ieee.org/document/11003913/ |
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