基于自适应特征融合金字塔与注意力机制的输电线路绝缘子缺陷检测方法

doi:10.11996/JG.j.2095-302X.2025050950

图学学报 ›› 2025, Vol. 46 ›› Issue (5): 950-959.DOI: 10.11996/JG.j.2095-302X.2025050950

• 图像处理与计算机视觉 • 上一篇下一篇

基于自适应特征融合金字塔与注意力机制的输电线路绝缘子缺陷检测方法

翟永杰(), 翟邦朝, 胡哲东, 杨珂, 王乾铭(), 赵晓瑜

华北电力大学自动化系，河北保定 071003

收稿日期:2024-12-06 接受日期:2025-02-12 出版日期:2025-10-30 发布日期:2025-09-10
通讯作者:王乾铭(1995-)，男，讲师，博士。主要研究方向为计算机视觉与深度学习。E-mail：qianmingwang@ncepu.edu.cn
第一作者:翟永杰(1972-)，男，教授，博士。主要研究方向为电力视觉。E-mail：zhaiyongjie@ncepu.edu.cn
基金资助:
国家自然科学基金(62373151);河北省自然科学基金面上项目(F2023502010);中央高校基本科研业务费专项资金(2023JC006);中央高校基本科研业务费专项资金(2024MS136)

Adaptive feature fusion pyramid and attention mechanism-based method for transmission line insulator defect detection

ZHAI Yongjie(), ZHAI Bangchao, HU Zhedong, YANG Ke, WANG Qianming(), ZHAO Xiaoyu

Department of Automation, Huabei Electric Power University, Baoding Hebei 071003, China

Received:2024-12-06 Accepted:2025-02-12 Published:2025-10-30 Online:2025-09-10
First author：ZHAI Yongjie (1972-), professor, Ph.D. His main research interest covers power vision. E-mail：zhaiyongjie@ncepu.edu.cn
Supported by:
National Natural Science Foundation of China(62373151);Hebei Provincial Natural Science Foundation general project(F2023502010);Special Fund for Basic Scientific Research of Central Universities(2023JC006);Special Fund for Basic Scientific Research of Central Universities(2024MS136)

摘要/Abstract

摘要：

针对输电线路绝缘子缺陷样本中存在的复杂背景干扰及缺陷区域尺度不一问题，提出了一种基于自适应融合特征金字塔与注意力机制的输电线路绝缘子缺陷检测方法。首先，利用自适应融合模块(AF)来处理不同尺度的特征信息，并将其集成到特征金字塔网络之中，以缓解绝缘子航拍图像中存在的缺陷区域尺度不一问题。然后，基于注意力机制的缺陷特征细化模块(DFRM)，通过增大感受野以及捕获缺陷区域的上下文特征来应对复杂背景噪声所带来的干扰。最后，将改进后的算法应用到真实输电线路绝缘子缺陷数据集进行实验。实验结果表明，该方法在绝缘子缺陷检测任务中优于其他方法，相较于基线模型准确率提升了5.7%，为电网智能巡检提供了一种有效方案。

关键词: 绝缘子缺陷, 特征融合, 注意力机制, 目标检测, 多尺度特征

Abstract:

To address the challenges of complex background interference and varying defect region scales in transmission line insulator samples, a method for transmission line insulator defect detection based on an adaptive fusion feature pyramid and attention mechanism was proposed. First, an adaptive fusion module (AF) was introduced to process multi-scale feature information, which was integrated into the feature pyramid network to mitigate the inconsistencies of defect region scales in aerial images of insulators. Next, a defect feature refinement module (DFRM) based on an attention mechanism was designed to handle interference from complex background noise by expanding the receptive field and capturing the contextual features of defective regions. Finally, the improved algorithm was validated on a real-world transmission line insulator defect dataset. Experimental results demonstrated that the proposed method outperformed existing approaches in insulator defect detection, achieving a 5.7% improvement in accuracy compared to the baseline model. These findings offered an effective solution for intelligent inspection in power grid systems.

Key words: insulator defect, feature fusion, attention mechanism, object detection, multi-scale feature

中图分类号:

翟永杰, 翟邦朝, 胡哲东, 杨珂, 王乾铭, 赵晓瑜. 基于自适应特征融合金字塔与注意力机制的输电线路绝缘子缺陷检测方法[J]. 图学学报, 2025, 46(5): 950-959.

ZHAI Yongjie, ZHAI Bangchao, HU Zhedong, YANG Ke, WANG Qianming, ZHAO Xiaoyu. Adaptive feature fusion pyramid and attention mechanism-based method for transmission line insulator defect detection[J]. Journal of Graphics, 2025, 46(5): 950-959.

图/表 12

参考文献 39

[1]	王亚茹, 冯利龙, 宋晓轲, 等. TFD-YOLOv8: 一种用于输电线路的异物检测方法[J]. 图学学报, 2024, 45(5): 901-912. DOI
	WANG Y R, FENG L L, SONG X K, et al. TFD-YOLOv8: a transmission line foreign object detection method[J]. Journal of Graphics, 2024, 45(5): 901-912 (in Chinese). DOI
[2]	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. DOI
	ZHAO Z B, HAN Y, TANG C K. Cascade detection method for insulator defects in distribution lines based on improved YOLOv8[J]. Journal of Graphics, 2025, 46(1): 1-12 (in Chinese). DOI
[3]	翟永杰, 郭聪彬, 王乾铭, 等. 基于隐含空间知识融合的输电线路多金具检测方法[J]. 图学学报, 2023, 44(5): 918-927. DOI
	ZHAI Y Y, GUO C B, WANG Q M, et al. Multi-fitting detection method for transmission lines based on implicit spatial knowledge fusion[J]. Journal of Graphics, 2023, 44(5): 918-927 (in Chinese).
[4]	牛杭, 葛鑫雨, 赵晓瑜, 等. 基于改进YOLOv8的防振锤缺陷目标检测算法[J/OL]. 图学学报, 1-11 [2025-01-06]. http://kns.cnki.net/kcms/detail/10.1034.T.20241210.1142.002.html.
	NIU H, GE X Y, ZHAO X Y, et al. Vibration damper defect detection algorithm based on improved YOLOv8[J/OL]. Journal of Graphics, 1-11 [2025-01-06]. (in Chinese).
[5]	郝帅, 赵新生, 马旭, 等. 基于TR-YOLOv5的输电线路多类缺陷目标检测方法[J]. 图学学报, 2023, 44(4): 667-676. DOI
	HAO S, ZHAO X S, MA X, et al. Multi-class defect target detection method for transmission lines based on TR-YOLOv5[J]. Journal of Graphics, 2023, 44(4): 667-676 (in Chinese). DOI
[6]	GHASEMI Y, JEONG H, CHOI S H, et al. Deep learning- based object detection in augmented reality: a systematic review[J]. Computers in Industry, 2022, 139: 103661.
[7]	戚银城, 武学良, 赵振兵, 等. 嵌入双注意力机制的Faster R-CNN航拍输电线路螺栓缺陷检测[J]. 中国图象图形学报, 2021, 26(11): 2594-2604.
	QI Y C, WU X L, ZHAO Z B, et al. Bolt defect detection for aerial transmission lines using Faster R-CNN with an embedded dual attention mechanism[J]. Journal of Image and Graphics, 2021, 26(11): 2594-2604 (in Chinese).
[8]	王韵琳, 冯天波, 孙宁, 等. 融合注意力与多尺度特征的电力绝缘子缺陷检测方法[J]. 高电压技术, 2024, 50(5): 1933-1942.
	WANG Y L, FENG T B, SUN N, et al. Defect detection method for power insulators based on attention and multi-scale context information[J]. High Voltage Engineering, 2024, 50(5): 1933-1942 (in Chinese).
[9]	魏良玉, 邹国锋, 赵新宇, 等. 面向航拍图像的绝缘子弱特征缺陷两阶段检测方法[J]. 国外电子测量技术, 2023, 42(10): 25-34.
	WEI L Y, ZOU G F, ZHAO X Y, et al. Two-stage detection method for weak feature defects of insulators in aerial images[J]. Foreign Electronic Measurement Technology, 2023, 42(10): 25-34 (in Chinese).
[10]	杨露露, 马萍, 王聪, 等. 结合特征重用与重建的YOLO绝缘子检测方法[J]. 计算机工程, 2024, 50(7): 303-313. DOI
	YANG L L, MA P, WANG C, et al. Insulator detection method using yolo combining feature reuse and reconstruction[J]. Computer Engineering, 2024, 50(7): 303-313 (in Chinese). DOI
[11]	白晓静, 谢雅祺, 赵淼, 等. 基于局部特征深度信息的绝缘子小样本缺陷检测[J]. 电网技术, 2024, 48(2): 740-749.
	BAI X J, XIE Y Q, ZHAO M, et al. Few-shot insulator defect detection based on deep information of local features[J]. Power System Technology, 2024, 48(2)3: 740-749 (in Chinese).
[12]	翟永杰, 赵晓瑜, 王璐瑶, 等. IDD-YOLOv7: 一种用于输电线路绝缘子多缺陷的轻量化检测方法[J]. 图学学报, 2024, 45(1): 90-101. DOI
	ZHAI Y J, ZHAO X Y, WANG L Y, et al. IDD-YOLOv7: a lightweight method for multiple defect detection of insulators in transmission lines[J]. Journal of Graphics, 2024, 45(1): 90-101 (in Chinese). DOI
[13]	REN S Q, HE K M, GIRSHICK R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137-1149. DOI PMID
[14]	GUO M H, LU C Z, LIU Z N, et al. Visual attention network[J]. Computational Visual Media, 2023, 9(4): 733-752.
[15]	CHEN K, WANG J Q, PANG J M, et al. MMDetection: open MMLab detection toolbox and benchmark[EB/OL]. [2024- 09-23]. https://arxiv.org/abs/1906.07155.
[16]	LIU W, ANGUELOV D, ERHAN D, et al. SSD: single shot MultiBox detector[C]// The 14th European Conference on Computer Vision. Cham: Springer, 2016: 21-37.
[17]	REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: unified, real-time object detection[C]// 2016 IEEE Conference on Computer Vision and Pattern Recognition. New York: IEEE Press, 2016: 779-788.
[18]	CAI Z W, VASCONCELOS N. Cascade R-CNN: delving into high quality object detection[C]// 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New York: IEEE Press, 2018: 6154-6162.
[19]	LIN T Y, GOYAL P, GIRSHICK R, et al. Focal loss for dense object detection[C]// 2017 IEEE International Conference on Computer Vision. New York: IEEE Press, 2017: 2999-3007.
[20]	LU X, LI B Y, YUE Y X, et al. Grid R-CNN[C]// 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New York: IEEE Press, 2019: 7355-7364.
[21]	TIAN Z, SHEN C H, CHEN H, et al. FCOS: fully convolutional one-stage object detection[C]// 2019 IEEE/CVF International Conference on Computer Vision. New York: IEEE Press, 2019: 9626-9635.
[22]	ZHANG H K, CHANG H, MA B P, et al. Dynamic R-CNN: towards high quality object detection via dynamic training[C]// The 16th European Conference on Computer Vision. Cham: Springer, 2020: 260-275.
[23]	SUN P Z, ZHANG R F, JIANG Y, Et al. Sparse R-CNN: end-to-end object detection with learnable proposals[C]// 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New York: IEEE Press, 2021: 14449-14458.
[24]	FENG C J, ZHONG Y J, GAO Y, et al. TOOD: task-aligned one-stage object detection[C]// 2021 IEEE/CVF International Conference on Computer Vision. New York: IEEE Press, 2021: 3490-3499.
[25]	LYU C, ZHANG W, HUANG H, et al. Rtmdet: An empirical study of designing real-time object detectors[EB/OL]. (2022-12-14) [2024-09-23]. https://arxiv.org/abs/2212.07784.
[26]	JOCHER G. Ultralytics YOLOv5: version 7.0[EB/OL]. [2024-09-23]. https://docs.ultralytics.com/zh/models/yolov5/#citations-and-acknowledgements.
[27]	JOCHER G, CHAURASIA A, QIU J. Ultralytics YOLO:version 8.0.0[EB/OL]. [2024-09-23]. https://docs.ultralytics.com/zh/models/yolov8/#citations-and-acknowledgements.
[28]	WANG C Y, YEH I H, LIAO H Y M. YOLOv9: learning what you want to learn using programmable gradient information[C]// The 18th European Conference on Computer Vision. Cham: Springer, 2024: 1-21.
[29]	WANG A, CHEN H, LIU L H, et al. YOLOv10:real-time end-to-end object detection[EB/OL]. [2024-12-05]. https://dblp.uni-trier.de/db/conf/nips/neurips2024.html#WangCLCLHD24.
[30]	VIEIRA-E-SILVA A L B, de CASTRO FELIX H, de MENEZES CHAVES T, et al. STN PLAD: a dataset for multi-size power line assets detection in high-resolution UAV Images[C]// 2021 34th SIBGRAPI Conference on Graphics, Patterns and Images. New York: IEEE Press, 2021: 215-222.
[31]	ZHU X K, LYU S C, WANG X, et al. TPH-YOLOv5: improved YOLOv5 based on transformer prediction head for object detection on drone-captured scenarios[C]// 2021 IEEE/CVF International Conference on Computer Vision. New York: IEEE Press, 2021: 2778-2788.
[32]	LI L L, WANG Z F, ZHANG T T. GBH-YOLOv5: ghost convolution with BottleneckCSP and tiny target prediction head incorporating YOLOv5 for PV panel defect detection[J]. Electronics, 2023, 12(3): 561.
[33]	LI C Y, LI L L, JIANG H L, et al. YOLOv6:a single-stage object detection framework for industrial applications[EB/OL]. [2024-12-05]. https://arxiv.org/abs/2209.02976.
[34]	WANG C Y, BOCHKOVSKIY A, LIAO H Y M. YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors[C]// 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2023: 7464-7475.
[35]	JOCHER G, QIU J, CHAURASIA A, et al. Ultralytics YOLO version 8.3.0[EB/OL]. [2024-12-05]. https://github.com/ultralytics/ultralytics.
[36]	LIU S, QI L, QIN H Y, et al. Path aggregation network for instance segmentation[C]// 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New York: IEEE Press, 2018: 8759-8768.
[37]	WANG J Q, CHEN K, XU R, et al. CARAFE: content-aware reassembly of features[C]// 2019 IEEE/CVF International Conference on Computer Vision. New York: IEEE Press, 2019: 3007-3016.
[38]	GHIASI G, LIN T Y, Le Q V. DropBlock: a regularization method for convolutional networks[C]// The 32nd International Conference on Neural Information Processing Systems. Red Hook: Curran Associates Inc., 2018: 10750-10760.
[39]	SUN K, ZHAO Y, JIANG B, et al. High-resolution representations for labeling pixels and regions[EB/OL]. (2019-04-09) [2024-12-05]. https://arxiv.org/abs/1904.04514.

硬件名称	型号	数量
CPU	英特尔至强6148处理器	1
内存	三星DDR4内存条16 G	8
显卡	NVIDIA GeForce RTX 3090	1
硬盘	Samsung SSD 980 PRO 2TB	1

硬件名称	型号	数量
CPU	英特尔至强6148处理器	1
内存	三星DDR4内存条16 G	8
显卡	NVIDIA GeForce RTX 3090	1
硬盘	Samsung SSD 980 PRO 2TB	1

模型	mAP/%	mAP50/%	mAP75/%	AR1/%	AR100/%	AP50-damage/%	AP50-drop/%	FPS
Cascade R-CNN^[18]	40.7	70.2	42.3	42.5	48.2	51.7	88.7	27.2
SSD	39.9	72.2	39.6	42.8	49.5	55.9	88.5	27.1
RetinaNet^[19]	43.4	76.7	45.1	44.3	56.9	65.9	87.6	27.0
Grid R-CNN^[20]	40.0	72.3	39.1	41.7	49.1	56.0	88.5	27.2
FCOS^[21]	40.4	72.8	40.9	42.9	53.8	58.6	86.9	27.2
Dynamic R-CNN^[22]	40.1	74.8	40.2	41.7	46.8	61.6	88.0	27.2
Sparse R-CNN^[23]	41.7	77.3	40.0	43.4	60.2	64.1	90.5	27.2
TOOD^[24]	43.3	75.7	45.3	43.8	56.5	61.7	89.6	27.2
RTMDet^[25]	44.3	72.9	48.5	44.3	58.8	54.9	90.8	27.9
YOLOv5s^[26]	40.7	67.2	46.5	42.0	51.6	48.2	86.2	46.7
YOLOv8s^[27]	42.2	70.5	45.3	42.7	51.7	54.0	87.0	49.3
YOLOv9s^[28]	42.0	71.4	47.6	43.1	52.0	56.7	86.0	27.0
YOLOv10s^[29]	40.6	70.0	42.4	42.8	50.3	52.8	87.1	38.3
Faster R-CNN(基线)	38.8	75.0	34.0	41.1	47.4	58.7	91.3	23.7
AFAM-Net(本文方法)	40.3	80.7	37.5	42.9	47.6	69.5	91.9	27.2

模型	mAP/%	mAP50/%	mAP75/%	AR1/%	AR100/%	AP50-damage/%	AP50-drop/%	FPS
Cascade R-CNN^[18]	40.7	70.2	42.3	42.5	48.2	51.7	88.7	27.2
SSD	39.9	72.2	39.6	42.8	49.5	55.9	88.5	27.1
RetinaNet^[19]	43.4	76.7	45.1	44.3	56.9	65.9	87.6	27.0
Grid R-CNN^[20]	40.0	72.3	39.1	41.7	49.1	56.0	88.5	27.2
FCOS^[21]	40.4	72.8	40.9	42.9	53.8	58.6	86.9	27.2
Dynamic R-CNN^[22]	40.1	74.8	40.2	41.7	46.8	61.6	88.0	27.2
Sparse R-CNN^[23]	41.7	77.3	40.0	43.4	60.2	64.1	90.5	27.2
TOOD^[24]	43.3	75.7	45.3	43.8	56.5	61.7	89.6	27.2
RTMDet^[25]	44.3	72.9	48.5	44.3	58.8	54.9	90.8	27.9
YOLOv5s^[26]	40.7	67.2	46.5	42.0	51.6	48.2	86.2	46.7
YOLOv8s^[27]	42.2	70.5	45.3	42.7	51.7	54.0	87.0	49.3
YOLOv9s^[28]	42.0	71.4	47.6	43.1	52.0	56.7	86.0	27.0
YOLOv10s^[29]	40.6	70.0	42.4	42.8	50.3	52.8	87.1	38.3
Faster R-CNN(基线)	38.8	75.0	34.0	41.1	47.4	58.7	91.3	23.7
AFAM-Net(本文方法)	40.3	80.7	37.5	42.9	47.6	69.5	91.9	27.2

模型	AP50(杆塔)	AP50(绝缘子)	AP50(间隔棒)	AP50()防震锤)	AP50(塔牌)	mAP50
TPH-YOLOv5s^[31]	41.9	81.3	90.4	25.8	81.9	64.2
GBH-YOLOv5s^[32]	34.7	75.1	85.8	27.6	80.8	60.8
YOLOv6s^[33]	43.9	88.3	77.5	8.3	73.4	58.3
YOLOv7-tiny^[34]	37.6	72.6	79.8	13.8	71.8	55.1
YOLOv8s^[27]	46.4	78.5	91.8	27.1	78.2	64.4
YOLOv10s^[29]	39.2	89.0	91.4	24.1	73.9	63.5
YOLOv11s^[35]	42.7	88.7	88.0	25.4	83.9	65.7
Dynamic R-CNN^[22]	33.5	94.9	85.3	10.0	50.5	54.8
Sparse R-CNN^[23]	46.6	65.2	63.1	18.4	81.4	55.0
RetinaNet^[19]	36.0	83.6	82.7	27.7	78.8	61.8
TOOD^[24]	46.0	92.0	83.8	30.8	76.9	65.9
Faster R-CNN(基线)	27.5	93.9	82.3	7.9	50.5	52.4
AFAM-Net(本文方法)	31.8	95.8	84.3	43.3	85.6	68.1

基于自适应特征融合金字塔与注意力机制的输电线路绝缘子缺陷检测方法

Adaptive feature fusion pyramid and attention mechanism-based method for transmission line insulator defect detection

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 12

参考文献 39

相关文章 15

编辑推荐

Metrics

本文评价

模型	mAP	mAP50	mAP75	mAP50- damage	mAP50- drop
基线模型	38.8	75.0	34.0	58.7	91.3
+AF-FPN	41.3	80.2	36.9	68.7	91.8
+DFRM	39.5	80.1	34.3	70.3	90.0
本文方法	40.3	80.7	37.5	69.5	91.9

[1]	刘伯凯, 殷雪峰, 孙传昱, 葛慧林, 魏子麒, 姜雨彤, 朴海音, 周东生, 杨鑫. 基于深度强化学习的无人机三维场景导航方法研究[J]. 图学学报, 2025, 46(5): 1010-1017.
[2]	左屿琪, 张云峰, 张秋悦, 徐英城. 基于超图表示学习和Transformer模型优化的知识感知推荐[J]. 图学学报, 2025, 46(5): 1050-1060.
[3]	郭瑞东, 蓝贵文, 范冬林, 钟展, 徐梓睿, 任新月. 基于特征聚焦扩散网络的电力巡检目标检测算法[J]. 图学学报, 2025, 46(4): 719-726.
[4]	闫卓越, 刘骊, 付晓东, 刘利军, 彭玮. 三维人体姿态和形状估计的分层注意力时空特征融合算法[J]. 图学学报, 2025, 46(4): 746-755.
[5]	杨佳熙, 于乐天, 包骐瑞, 毕胜, 麻晓斗, 杨晟琦, 姜雨彤, 方建儒, 魏小鹏, 杨鑫. 面向高光子通量环境的目标深度估计方法[J]. 图学学报, 2025, 46(4): 756-762.
[6]	廖国琼, 黄龙杰, 李清新, 辜勇, 李海波. 面向单目可见光环境的自适应双手重建网络[J]. 图学学报, 2025, 46(4): 837-846.
[7]	牛杭, 葛鑫雨, 赵晓瑜, 杨珂, 王乾铭, 翟永杰. 基于改进YOLOv8的防振锤缺陷目标检测算法[J]. 图学学报, 2025, 46(3): 532-541.
[8]	于冰, 程广, 黄东晋, 丁友东. 基于双流网络融合的三维人体网格重建[J]. 图学学报, 2025, 46(3): 625-634.
[9]	雷玉林, 刘利刚. 基于深度强化学习的可缓冲的物体运输和装箱[J]. 图学学报, 2025, 46(3): 697-708.
[10]	张立立, 杨康, 张珂, 魏薇, 李晶, 谭洪鑫, 张翔宇. 面向柴油车辆排放黑烟的改进型YOLOv8检测算法研究[J]. 图学学报, 2025, 46(2): 249-258.
[11]	郭业才, 胡晓伟, 毛湘南. 多尺度密集交互注意力残差真实图像去噪网络[J]. 图学学报, 2025, 46(2): 279-287.
[12]	翟永杰, 王璐瑶, 赵晓瑜, 胡哲东, 王乾铭, 王亚茹. 基于级联查询-位置关系的输电线路多金具检测方法[J]. 图学学报, 2025, 46(2): 288-299.
[13]	潘树焱, 刘立群. MSFAFuse：基于多尺度特征信息与注意力机制的SAR和可见光图像融合模型[J]. 图学学报, 2025, 46(2): 300-311.
[14]	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.
[15]	董佳乐, 邓正杰, 李喜艳, 王诗韵. 基于频域和空域多特征融合的深度伪造检测方法[J]. 图学学报, 2025, 46(1): 104-113.