基于类内稀疏先验与改进YOLOv8的绝缘子红外图像检测方法

doi:10.11996/JG.j.2095-302X.2025061247

图学学报 ›› 2025, Vol. 46 ›› Issue (6): 1247-1256.DOI: 10.11996/JG.j.2095-302X.2025061247

• 图像处理与计算机视觉 • 上一篇下一篇

基于类内稀疏先验与改进YOLOv8的绝缘子红外图像检测方法

赵振兵¹^,²^,³(), 欧阳文斌¹, 冯烁¹, 李浩鹏¹^,², 马隽⁴

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
⁴ 河北医科大学干细胞研究中心、人体解剖学教研室，河北石家庄 050017

收稿日期:2025-01-04 接受日期:2025-04-16 出版日期:2025-12-30 发布日期:2025-12-27
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(61871182);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)

A thermal image detection method for insulators incorporating within-class sparse prior knowledge and improved YOLOv8

ZHAO Zhenbing¹^,²^,³(), Ouyang Wenbin¹, FENG Shuo¹, LI Haopeng¹^,², MA Jun⁴

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
⁴ Stem Cell Research Center, Human Anatomy Department, Hebei Medical University, Shijiazhuang Hebei 050017, China

Received:2025-01-04 Accepted:2025-04-16 Published:2025-12-30 Online:2025-12-27
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(61871182);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：

为了实现低分辨率绝缘子红外图像的高精度检测，提出了基于类内稀疏先验和改进YOLOv8的红外多尺度绝缘子检测方法。针对绝缘子图像存在局部密集导致漏检的问题，提出了通用性的类内稀疏先验，将先验知识和训练数据结合，使模型感知绝缘子独有的几何特征和形态信息，可以无计算代价地提升目标检测模型的精度，并提供一种绝缘子数据样本的规范化标注方法；针对红外图像分辨率低导致特征提取困难的问题，将鲁棒特征下采样模块替代卷积下采样，保留细粒度细节信息，增强关键特征图的鲁棒表示；针对绝缘子尺度变化大且存在目标遮挡的问题，设计了wise-MPDIoU改进边框损失函数，以改善模型对不同尺寸绝缘子的定位能力。实验数据表明，相比于基线模型，在AP50和AP50:95指标上分别提升3.3和3.5个百分点，为绝缘子热像检测提供了新的方案。

关键词: 绝缘子, 红外图像, 类内稀疏先验, 目标检测, 深度学习

Abstract:

To achieve high-precision detection of low-resolution thermal image of insulators, an infrared multi-scale insulator detection method was proposed based on intra-class sparse prior and improved YOLOv8. To address the issue of missed detections due to local density in insulator images, a universal intra-class sparse prior was proposed, combining prior knowledge with training data. This enabled the model to perceive the unique geometric features and morphological information of the insulator, enhancing the accuracy of the target detection model without additional computational cost, and provided a standardized annotation method for insulator data samples. To tackle the difficulty of feature extraction from low-resolution infrared images, a robust feature downsampling module was employed to replace convolutional downsampling, preserving fine-grained detail information and enhancing the robust representation of key feature maps. For the problem of large-scale variations and occlusion in insulator targets, a wise-MPDIoU was utilized, improving the bounding-box loss function and the model’s ability to localize insulators of different sizes. Experimental data demonstrated that, compared to the baseline model, the proposed method achieved improvements of 3.3 and 3.5 percentage points in AP50 and AP50:95 metrics, respectively, providing a new solution for insulator thermal image detection.

Key words: insulator, thermal image, within-class sparse prior knowledge, object detection, deep learning

中图分类号:

赵振兵, 欧阳文斌, 冯烁, 李浩鹏, 马隽. 基于类内稀疏先验与改进YOLOv8的绝缘子红外图像检测方法[J]. 图学学报, 2025, 46(6): 1247-1256.

ZHAO Zhenbing, Ouyang Wenbin, FENG Shuo, LI Haopeng, MA Jun. A thermal image detection method for insulators incorporating within-class sparse prior knowledge and improved YOLOv8[J]. Journal of Graphics, 2025, 46(6): 1247-1256.

图/表 13

图1 数据集的3种标注方法((a) 原始标注方法；(b) 稀疏先验；(c) 类内稀疏先验)

Fig. 1 Three annotation methods for data sets ((a) Original annotation method; (b) Sparse prior; (c) Within-class sparse prior)

图2 类内稀疏先验的热力图可视化((a) 原始标注方法；(b) 稀疏先验；(c) 类内稀疏先验)

Fig. 2 Heat map visualization within-class sparse prior knowledge ((a) Original annotation method; (b) Sparse prior; (c) Within-class sparse prior)

图3 不同数据集先验知识选择的方法

Fig. 3 The method of prior knowledge selection for different data sets

图4 基于类内稀疏先验和改进YOLOv8的红外多尺度绝缘子检测方法

Fig. 4 A thermal image detection method for insulators incorporating within-class sparse prior knowledge and improved YOLOv8

图5 浅层鲁棒特征下采样模块

Fig. 5 Shallow robust feature downsampling module

图6 深层鲁棒特征下采样模块

Fig. 6 Deep robust feature downsampling module

表1 类内稀疏先验通用性的实验

Table 1 Experiments on sparse prior generality within-class

模型	标注方法	P	R	AP50/%	AP50:95/%	FPS/(帧·s^-1)	GFLPOs	Parameters/M
	①	86.9	72.4	80.9	57.6	87.72
RT-DETRr18	②	88.9	77.6	85.5	59.9	75.19	56.9	19.9
	③	92.3	78.4	87.9	62.2	90.91
	①	82.6	70.5	82.1	57.6	161.29
YOLOv10n	②	80.0	75.7	82.2	56.4	123.46	8.2	2.7
	③	84.5	79.9	86.7	60.4	151.52
	①	91.1	75.2	82.9	49.2	588.24
YOLOv7t	②	87.9	78.5	85.1	54.0	555.56	13.0	6.0
	③	89.8	82.2	87.1	54.8	555.56
	①	94.8	81.7	89.1	61.7	123.46
YOLOV8n	②	87.6	80.6	88.0	61.4	151.52	8.1	3.0
	③	93.0	86.5	91.4	63.2	140.85

表2 模型结构消融实验结果

Table 2 The ablation experimental result of the model structure

YOLOv8n	类内稀疏先验	RFD	Wise-MPDIoU	P	R	AP50/%	AP50:95/%	FPS/(帧·s^-1)	GFLPOs	Parameters/M
√				94.8	81.7	89.1	61.7	123.46	8.1	3.01
√	√			93.0	86.5	91.4	63.2	140.85	8.1	3.01
√	√	√		93.5	86.2	92.1	63.9	121.95	8.1	3.02
√	√		√	95.6	85.8	92.4	64.1	144.93	9.6	3.01
√	√	√	√	93.6	87.6	92.4	65.2	120.48	9.6	3.02

图7 消融实验结果可视化((a) 原图；(b) 基线；(c) 融合类内稀疏先验；(d) 融合类内稀疏先验且更换RFD；(e) 融合类内稀疏先验且更换Wise-MPDIoU；(f) 本文方法)

Fig. 7 Visualization of ablation results ((a) Original image; (b) Baseline; (c) Within-class sparse prior knowledge; (d) Within-class sparse prior knowledge and RFD; (e) Within-class sparse prior knowledge and Wise-MPDIoU; (f) Ours)

表3 不同下采样方式在YOLOv8中的对比结果

Table 3 Comparison results of different downsampling methods in YOLOv8

方法	AP50/%	AP50:95/%	GFLPOs
ODConv	87.3	60.7	5.8
HWD	91.5	62.6	7.7
WaveletPool	91.1	63.4	7.4
LDConv	91.0	62.3	8.0
RFD	92.1	63.9	8.1

表4 多种损失函数在YOLOv8中的对比结果

Table 4 Comparison of multiple loss functions in YOLOv8

方法	AP50/%	AP50:95/%	GFLPOs
shapeIoU	92.2	63.8	8.1
focalerWIoU	92.1	63.8	8.1
SlideLoss	91.9	63.8	8.1
WIoU	91.1	63.6	8.1
MPDIoU	92.1	63.7	8.1
Wise-MPDIoU	92.4	64.1	9.6

表5 多种主流模型的对比试验

Table 5 Comparative test of multi-medium mainstream models

方法	P	R	AP50/%	AP50:95/%	FPS/(帧·s^-1)	GFLPOs	Parameters/M
YOLOv9	89.5	78.8	86.4	57.9	163.93	11.7	2.80
RT-DETR	92.3	78.4	87.9	62.2	90.91	56.9	19.90
YOLOv10	84.5	79.9	86.7	60.4	151.52	8.2	2.70
YOLOv5	93.7	83.4	89.7	56.7	250.00	4.1	1.80
YOLOv7	89.8	82.2	87.1	54.8	555.56	13.0	6.00
本文方法	93.6	87.6	92.4	65.2	144.93	9.6	3.02

图8 绝缘子热像可视化结果((a) 原图；(b) YOLOv9；(c) RT-DETR；(d) YOLOv10；(e) YOLOv5；(f) YOLOv7；(g) 本文方法)

Fig. 8 Insulator thermal image visualization results ((a) Original image; (b) YOLOv9; (c) RT-DETR; (d) YOLOv10; (e) YOLOv5; (f) YOLOv7; (g) Ours)

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基于类内稀疏先验与改进YOLOv8的绝缘子红外图像检测方法

A thermal image detection method for insulators incorporating within-class sparse prior knowledge and improved YOLOv8

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