Research on Application of Improved YOLOv11n in Detection of Main Defects of Insulators on Transmission Lines

LIU Hongzhi; MA Yue; QIU Bin; WANG Kai

doi:10.13296/j.1001-1609.hva.2025.10.016

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Research on Application of Improved YOLOv11n in Detection of Main Defects of Insulators on Transmission Lines

Digital Twin Technology and Applications for Multi-scenario in New Power System | 更新时间：2025-10-21

- Research on Application of Improved YOLOv11n in Detection of Main Defects of Insulators on Transmission Lines
- High Voltage Apparatus Vol. 61, Issue 10, Pages: 149-158(2025)
- 作者机构：
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- DOI：10.13296/j.1001-1609.hva.2025.10.016
  CLC：
- Received：17 March 2025，
  
  Revised：2025-06-08，
  
  Published：16 October 2025
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LIU Hongzhi, MA Yue, QIU Bin, et al. Research on Application of Improved YOLOv11n in Detection of Main Defects of Insulators on Transmission Lines[J]. High voltage apparatus, 2025, 61(10): 149-158.
DOI：

LIU Hongzhi, MA Yue, QIU Bin, et al. Research on Application of Improved YOLOv11n in Detection of Main Defects of Insulators on Transmission Lines[J]. High voltage apparatus, 2025, 61(10): 149-158. DOI： 10.13296/j.1001-1609.hva.2025.10.016.

摘要

随着电力巡检智能化需求的增长，深度学习缺陷检测技术在输电线路巡检中得到广泛应用。然而，传统目标检测算法在小目标检测中，尤其在复杂背景和目标信息稀缺环境下，存在检测精度不高的问题。文中在YOLOv11基础上进行3项改进，提出一种改进YOLOv11的绝缘子缺陷检测方法。首先，引入C2PSA-CAA模块，结合通道注意力与特征聚合技术，提升小目标特征提取能力；其次，改进Focal-EIOU损失函数，优化了长宽比匹配，提高了预测框精度及对难检测目标的学习能力；最后，引入HS-FPN特征融合模块，提升了不同尺度小目标检测性能。实验结果表明，改进后的YOLOv11n模型在精度上较原YOLOv11模型精度提升了3.7%，准确率提升了1.4%，召回率提升了5.4%，为无人机电力巡检中的智能化应用提供了有效的技术支撑。

Abstract

With the growing demand for intelligent power inspection

deep learning-based defect detection technology has been widely applied in inspectino of ransmission line. However

traditional target detection algorithms still have the problem of low detection accuracy in the small targets detection

with complex backgrounds and sparse target information environments in particular. In this paper

three improvements based on YOLOv11 is made and a insulator defect detection method based on YOLOv11 is proposed First

a C2PSA-CAA module is introduced

combining channel attention and feature aggregation technology to enhance small target feature extraction. Then

the improved Focal-EIOU loss function is designed to optimize aspect ratio matching

improve bounding box accuracy

and strengthen learning for hard-to-detect targets. Finally

an HS-FPN feature fusion module is incorporated to improve detection performance for small target at different scales. Experimental results show that the improved YOLOv11n model achieves a 3.7% increase in precision

1.4% in accuracy

and a 5.4% in recall compared to the original YO-LOv11 model

providing effective technical support for intelligent applications in drone-powered inspection.

关键词

Keywords

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