基于改进YOLOv8的配电线路绝缘子缺陷级联检测方法

doi:10.11996/JG.j.2095-302X.2025010001

图学学报 ›› 2025, Vol. 46 ›› Issue (1): 1-12.DOI: 10.11996/JG.j.2095-302X.2025010001

• 图像处理与计算机视觉 • 下一篇

基于改进YOLOv8的配电线路绝缘子缺陷级联检测方法

赵振兵¹^,²^,³(), 韩钰¹, 唐辰康¹

1.华北电力大学电子与通信工程系，河北保定 071003
2.华北电力大学河北省电力物联网技术重点实验室，河北保定 071003
3.华北电力大学复杂能源系统智能计算教育部工程研究中心，河北保定 071003

收稿日期:2024-07-23 接受日期:2024-10-07 出版日期:2025-02-28 发布日期:2025-02-14
第一作者:赵振兵(1979-)，男，教授，博士。主要研究方向为电力视觉技术等。E-mail：zhaozhenbing@ncepu.edu.cn
基金资助:
国家自然科学基金(U21A20486);国家自然科学基金(62373151);国家自然科学基金(62371188);国家自然科学基金(62303184);河北省自然科学基金(F2021502008);河北省自然科学基金(F2021502013);中央高校基本科研业务费专项资金(2023JC006)

Cascade detection method for insulator defects in distribution lines based on improved YOLOv8

ZHAO Zhenbing¹^,²^,³(), HAN Yu¹, TANG Chenkang¹

1. Department of Electronic and Communication Engineering, North China Electric Power University, Baoding Hebei 071003, China
2. Hebei Key Laboratory of Power Internet of Things Technology, North China Electric Power University, Baoding Hebei 071003, China
3. Engineering Research Center of Intelligent Computing for Complex Energy Systems, Ministry of Education, North China Electric Power University, Baoding Hebei 071003, China

Received:2024-07-23 Accepted:2024-10-07 Published:2025-02-28 Online:2025-02-14
First author：ZHAO Zhenbing (1979-), professor, Ph.D. His main research interests cover computer vision technology in electric power system, etc. E-mail：zhaozhenbing@ncepu.edu.cn
Supported by:
National Natural Science Foundation of China(U21A20486);National Natural Science Foundation of China(62373151);National Natural Science Foundation of China(62371188);National Natural Science Foundation of China(62303184);Natural Science Foundation of Hebei Province of China(F2021502008);Natural Science Foundation of Hebei Province of China(F2021502013);Fundamental Research Funds for the Central Universities(2023JC006)

摘要/Abstract

摘要：

针对无人机航拍配电线路时因安全限制导致背景复杂动态、绝缘子缺陷形态不规则、缺陷特征不明显与缺陷信息难捕捉的问题，提出了一种基于改进YOLOv8的配电线路绝缘子缺陷级联检测方法。在第一阶段，通过YOLOv8模型自动提取绝缘子部件图像，为第二阶段绝缘子缺陷检测提供准确的输入，摒除冗余背景信息的影响。在第二阶段，利用ConvNeXt V2主干网络提升模型对不规则形态目标的识别能力，提升网络的特征提取能力；通过在特征融合过程中加入边缘知识融合模块，精准提取缺陷边缘信息；设计自适应形状IoU增强方法，采用自适应训练样本选择策略优化正负样本比例，并使用充分考虑边界框回归样本自身形状和尺度等固有属性的Shape-IoU损失函数，使模型聚焦目标本质特征，改善模型漏检误检情况，提高检测的准确性和鲁棒性。经实验证明，基于改进YOLOv8的配电线路绝缘子缺陷级联检测方法比基线模型平均精确率提高了17.3%，有效提升配电线路绝缘子缺陷检测准确率，为电力系统的安全维护提供了有力的技术支持。

关键词: 配电线路, 绝缘子缺陷检测, YOLOv8, ConvNeXt V2, 边缘知识融合, 自适应形状IoU增强

Abstract:

To address the issues of complex and dynamic backgrounds due to safety constraints, irregular shapes of insulator defects, indistinct defect features, and difficulty in capturing defect information during aerial photography of power distribution lines using unmanned aerial vehicles, a cascaded detection method for insulator defects in power distribution lines based on an improved YOLOv8 was proposed. In the first stage, the YOLOv8 model automatically extracted images of insulator components, providing accurate inputs for the second stage of insulator defect detection and eliminating the influence of redundant background information. In the second stage, the ConvNeXt V2 backbone network was utilized to enhance the model's ability to recognize irregularly shaped targets and improve its feature extraction capabilities. By incorporating the edge knowledge fusion module into the feature fusion process, precise extraction of defect edge information was achieved. Furthermore, an adaptive shape IoU enhancement method was designed, adopting an adaptive training sample selection strategy to optimize the ratio of positive and negative samples. Additionally, the Shape-IoU loss function was employed, considering the inherent attributes of bounding box regression samples such as shape and scale, enabling the model to focus on essential target features, thereby improving the detection accuracy and robustness by reducing missed and false detections. Experimental results demonstrated that the proposed cascaded detection method for insulator defects in power distribution lines based on the improved YOLOv8 achieved a 17.3% increase in average precision compared to baseline models, effectively enhancing the accuracy of insulator defect detection in power distribution lines and providing robust technical support for the safe maintenance of power systems.

Key words: distribution lines, insulator defect detection, YOLOv8, ConvNeXt V2, edge knowledge fusion, adaptive shape IoU enhancement

中图分类号:

赵振兵, 韩钰, 唐辰康. 基于改进YOLOv8的配电线路绝缘子缺陷级联检测方法[J]. 图学学报, 2025, 46(1): 1-12.

ZHAO Zhenbing, HAN Yu, TANG Chenkang. Cascade detection method for insulator defects in distribution lines based on improved YOLOv8[J]. Journal of Graphics, 2025, 46(1): 1-12.

图/表 12

图1 配电线路绝缘子及其缺陷数据集示例((a)无人机航拍原图；(b)绝缘子损伤缺陷；(c)绝缘子污秽缺陷)

Fig. 1 Examples from the insulator and defect dataset of distribution lines ((a) Aerial original image of UAV; (b) Insulator damage defect; (c) Insulator pollution defect)

图2 配电线路绝缘子缺陷两阶段级联检测方法示意图

Fig. 2 Schematic diagram of two-stage cascade detection method for insulator defects in distribution lines

图3 改进YOLOv8网络模型架构图

Fig. 3 Improved YOLOv8 network model architecture diagram

图4 ConvNeXt V2网络架构图

Fig. 4 Network architecture diagram of ConvNeXt V2 ((a) ConvNeXt V2; (b) ConvNeXt V2 block; (c) Downsample)

图5 边缘知识融合模块结构

Fig. 5 Structure of edge knowledge fusion module

表1 实验环境

Table 1 Experimental environment

名称	型号
操作系统	Ubuntu16.04.1
CPU	E5-2620 v4
GPU	TITAN Xp
CUDA	10.2
cuDNN	7.6.5
Python	3.8
深度学习框架	PyTorch 1.12.0

表2 数据集详细情况

Table 2 Dataset details

目标类别	绝缘子数据集			绝缘子缺陷数据集
目标类别	悬式绝缘子	柱式绝缘子	针式绝缘子	损伤绝缘子	污秽绝缘子	正常绝缘子
样本数量/个	3 831	5 333	2 489	430	1 160	463
标签名称	x_jyz	zh_jyz	zs_jyz	sunshang	wuhui	zq

表3 主干网络对比实验结果

Table 3 The comparison experiment results of backbone network

主干网络	P/%	R/%	mAP50/%	mAP50:95/%	Parameters/M	GFLOPs/%
ConvNeXt V2^[28]	69.8	67.2	75.2	67.1	5.7	14.1
EfficientViT^[34]	53.3	67.1	61.4	48.7	4.0	9.4
FasterNet^[35]	59.4	70.0	65.6	53.7	4.2	10.7
Timm	61.5	71.5	68.7	58.7	13.3	35.1
VanillaNet^[36]	61.8	66.6	69.6	58.3	23.9	96.7
LSKNet^[37]	53.1	74.3	67.6	56.5	5.9	19.7

表4 模型结构消融实验结果

Table 4 The ablation experimental result of the model structure

模块				P/%	R/%	AP/%			mAP50/%	mAP50:95/%	Parameters/M	GFLOPs/%
A	B	C	D	P/%	R/%	损伤	污秽	正常	mAP50/%	mAP50:95/%	Parameters/M	GFLOPs/%
√	-	-	-	51.9	67.3	48.7	80.0	56.7	61.8	52.1	3.0	8.1
√	√	-	-	69.8	67.2	68.8	87.1	69.7	75.2	67.1	5.7	14.1
√	√	√	-	69.8	70.0	72.3	87.3	69.6	76.4	67.2	6.3	14.1
√	√	-	√	70.2	68.0	70.4	87.8	69.2	75.8	66.7	5.6	14.1
√	√	√	√	70.3	75.1	75.2	88.2	73.9	79.1	69.8	6.3	14.1

表5 检测模型性能比较

Table 5 The comparison of detection model performances

方法	P/%	R/%	AP/%			mAP50/%	mAP50:95/%	Parameters/M	GFLOPs/%
方法	P/%	R/%	损伤	污秽	正常	mAP50/%	mAP50:95/%	Parameters/M	GFLOPs/%
YOLOv8n	51.9	67.3	48.7	80.0	56.7	61.8	52.1	3.0	8.1
YOLOv7-tiny	40.7	70.9	32.3	65.9	51.6	49.9	30.4	6.0	13.0
YOLOv6n	50.8	61.6	41.4	72.6	50.4	54.8	39.1	4.6	11.3
YOLOv5n	51.7	63.9	46.4	73.2	54.9	58.2	38.8	1.7	4.1
Faster R-CNN	-	-	43.6	60.7	47.8	50.7	-	-	-
YOLOv9-c^[31]	61.7	73.6	70.4	85.4	63.4	73.1	64.7	50.7	236.6
Ours	70.3	75.1	75.2	88.2	73.9	79.1	69.8	6.3	14.1

图6 绝缘子缺陷检测结果

Fig. 6 Detection results of insulator defects ((a) YOLOv8n; (b) YOLOv7; (c) YOLOv6; (d) YOLOv5; (e) Faster R-CNN; (f) YOLOv9-c; (g) Ours)

图7 损失曲线图((a)训练集回归损失曲线；(b)训练集分布焦点损失曲线；(c)训练集分类损失曲线；(d)验证集回归损失曲线；(e)验证集分布焦点损失曲线；(f)验证集分类损失曲线)

Fig. 7 Loss curve char t ((a) Regression loss curve of training set; (b) Distribution focal loss curve of training set; (c) Classification loss curve of training set; (d) Regression loss curve of validation set; (e) Distribution focal loss curve of validation set; (f) Classification loss curve of validation set)

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基于改进YOLOv8的配电线路绝缘子缺陷级联检测方法

Cascade detection method for insulator defects in distribution lines based on improved YOLOv8

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