An Insulator Burst Defect Detection Method Based on YOLOv4-MP

Xie Guo-bo; Lin Li; Lin Zhi-yi; He Di-xuan; Wen Gang

doi:10.12052/gdutxb.220079

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An Insulator Burst Defect Detection Method Based on YOLOv4-MP

更新时间：2026-05-27

- An Insulator Burst Defect Detection Method Based on YOLOv4-MP
- Vol. 40, Issue 2, Pages: 15-21(2023)
- 作者机构：
  
  1. 广东工业大学计算机学院,广东,广州,510006
  2. 云南电网有限责任公司电力科学研究院,云南,昆明,650011
- 作者简介：
- 基金信息：
- DOI：10.12052/gdutxb.220079
  CLC： TP391.4;TM216
- Online First：25 March 2023，
  
  Published：2023
- 稿件说明：
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谢国波, 林立, 林志毅, 贺笛轩, 文刚. 基于YOLOv4-MP的绝缘子爆裂缺陷检测方法[J]. 广东工业大学学报, 2023, 40(2): 15-21.

Xie Guo-bo, Lin Li, Lin Zhi-yi, et al. An Insulator Burst Defect Detection Method Based on YOLOv4-MP[J]. 2023, 40(2): 15-21.
谢国波, 林立, 林志毅, 贺笛轩, 文刚. 基于YOLOv4-MP的绝缘子爆裂缺陷检测方法[J]. 广东工业大学学报, 2023, 40(2): 15-21. DOI： 10.12052/gdutxb.220079.

Xie Guo-bo, Lin Li, Lin Zhi-yi, et al. An Insulator Burst Defect Detection Method Based on YOLOv4-MP[J]. 2023, 40(2): 15-21. DOI： 10.12052/gdutxb.220079.

摘要

针对输电线路绝缘子爆裂缺陷检测中缺陷目标小、背景复杂多样导致检测精度低的问题，提出了一种基于YOLOv4改进的检测算法YOLOv4-MP。首先，为减少复杂背景的干扰，在特征提取网络中嵌入Shuffle Attention注意力模块，使模型能够提取到更加有效的特征信息。其次，为增强特征融合的效果，在空间金字塔池化中引入带空洞的池化层，能够有效增大感受野。最后，为减少低层信息的丢失，采用Mish函数作为路径增强网络的激活函数。实验结果表明，YOLOv4-MP的平均精度均值(Mean Average Precision

mAP) 达到了93.60%，比YOLOv4算法提升了6.37%。与常用的检测算法相比，YOLOv4-MP具有更好的检测性能，对于绝缘子爆裂缺陷检测具有较大应用价值。

Abstract

Aiming at the problems of small defect targets and complex backgrounds

which leads to low accuracy in insulator burst defect detection

an improved detection algorithm YOLOv4-MP based on YOLOv4 is proposed. First

the Shuffle Attention module is included in the feature extraction network to limit the interference of complicated background

enabling the model to extract more effective feature information. Then

the spatial pyramid pooling is enhanced by the dilated pooling layer

which effectively increases the receptive field and improves the effect of feature fusion. Finally

the Mish function is utilized as the activation function of the path enhancement network to limit the loss of low-level information. The experimental results show that the mean average precision of YOLOv4-MP reaches 93.60%

which is 6.37% higher than that of the YOLOv4 algorithm. Compared with the commonly used detection algorithms

YOLOv4-MP has better detection performance and has high application value for the detection of insulator burst defects.

关键词

Keywords

references

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