Lightweight method for transmission line defect detection

doi:10.11772/j.issn.1001-9081.2025030340

Journal of Computer Applications ›› 2026, Vol. 46 ›› Issue (3): 969-979.DOI: 10.11772/j.issn.1001-9081.2025030340

• Multimedia computing and computer simulation • Previous Articles Next Articles

Lightweight method for transmission line defect detection

Ping HUANG¹(), Qing LI¹, Haifeng QIU¹, Chengsi WANG¹, Anzi HUANG¹, Long FAN²

^1.Shenzhen Power Supply Bureau Company Limited，Shenzhen Guangdong 518001，China
^2.School of Computer Science，Nanjing University，Nanjing Jiangsu 210023，China

Received:2025-04-03 Revised:2025-06-11 Accepted:2025-06-12 Online:2025-06-27 Published:2026-03-10
Contact: Ping HUANG
About author:LI Qing， born in 1988， M. S.， senior engineer. Her research interests include power informatization.
QIU Haifeng， born in 1981， M. S.， senior engineer. His research interests include power engineering.
WANG Chengsi， born in 1986， M. S.， senior engineer. His research interests include power engineering.
HUANG Anzi， born in 1980， M. S.， senior engineer. His research interests include power engineering.
FAN Long， born in 1994， Ph. D. His research interests include deep learning， image processing， ubiquitous computing.

轻量化输电线路缺陷检测方法

黄萍¹(), 李清¹, 邱海枫¹, 王程斯¹, 黄安子¹, 樊龙²

^1.深圳供电局有限公司，广东深圳 518001
^2.南京大学计算机学院，南京 210023

通讯作者: 黄萍
作者简介:李清（1988—），女，湖南衡阳人，高级工程师，硕士，主要研究方向：电力信息化
邱海枫（1981—），男，广东湛江人，高级工程师，硕士，主要研究方向：电力工程
王程斯（1986—），男，广东梅州人，高级工程师，硕士，主要研究方向：电力工程
黄安子（1980—），男，广东深圳人，高级工程师，硕士，主要研究方向：电力工程
樊龙（1994—），男，内蒙古呼和浩特人，博士，主要研究方向：深度学习、图像处理、普适计算。

Abstract

Abstract:

As the core transmission and distribution carrier of the power system， the operating condition of high-voltage transmission lines directly impacts the safety of the power grid. To address the problems of low efficiency and high missed rate in traditional manual inspection， a lightweight method for transmission line defect detection based on a two-stage multi-modal attention mechanism and dynamic feature decoupling was proposed. In the first stage， accurate localization of key components was achieved on the basis of an improved lightweight detection network， Light-YOLO. In the second stage， a dual-branch contrastive learning-based defect detection network， Dual-DifferNet， was built to achieve precise classification and identification of defects. In the design of Light-YOLO， a hybrid structure of hierarchical Separable Vision Transformer （SepViT） and deep Deformable Convolutional Network （DCN） was introduced， and by stacking local perception convolutional layers and global attention Transformer blocks alternately， the model’s modeling capability of long-range dependencies was enhanced while reducing computational cost， thereby improving the detection accuracy of small targets such as insulators and conductor splices effectively. For the defect classification task， in Dual-DifferNet， a dual-branch structure was adopted to embed a Spatial-Channel Dual Attention （SCDA） module in each branch， and the dual-modal feature interaction was promoted using a cross attention mechanism， thereby improving the robustness and generalization capability of defect identification. Experimental results show that the proposed method achieves a mean Average Precision （mAP@50） of 96.9%， which is 16.1 percentage points higher than that of the baseline model YOLOv8， with the floating-point operations reduced by 56.73%， fully verifying the method’s high detection accuracy， excellent computational efficiency， and deployment potential.

Key words: transmission line defect detection, hierarchical Separable Vision Transformer (SepViT), Bidirectional Recurrent Feature Pyramid Network (BR-FPN), dual attention, deformable convolution

摘要：

作为电力系统的核心输配电载体，高压输电线路的运行状态直接关系到电网安全。针对传统人工巡检效率低和漏检率高的问题，提出一种基于两阶段多模态注意力机制与动态特征解耦的轻量化输电线路缺陷检测方法。在第一阶段，基于改进型轻量检测网络Light-YOLO实现关键组件的精准定位；在第二阶段，构建基于双分支对比学习的缺陷检测网络Dual-DifferNet实现缺陷的精确分类与识别。在Light-YOLO的设计中，引入分层可分离视觉Transformer（SepViT）与深度可变形卷积网络（DCN）的混合结构，并通过交替堆叠局部感知卷积层与全局注意力Transformer块，在降低计算量的同时，增强模型对长程依赖关系的建模能力，从而有效提升绝缘子和导线接头等小目标的检测精度。针对缺陷分类任务，Dual-DifferNet采用双分支结构在每个分支中嵌入空间-通道双重注意力（SCDA）模块，利用交叉注意力机制促进双模态特征交互，从而提高缺陷识别的鲁棒性与泛化能力。实验结果表明，所提方法的平均精度均值（mAP@50）达到了96.9%，较基准模型YOLOv8提升16.1个百分点，同时浮点运算量降低了56.73%，充分验证了该方法在保证高精度检测的同时，具备优异的计算效率与部署潜力。

关键词: 输电线路缺陷检测, 分层可分离视觉Transformer, 双向递归特征金字塔网络, 双重注意力, 可变形卷积

CLC Number:

TP391

Ping HUANG, Qing LI, Haifeng QIU, Chengsi WANG, Anzi HUANG, Long FAN. Lightweight method for transmission line defect detection[J]. Journal of Computer Applications, 2026, 46(3): 969-979.

黄萍, 李清, 邱海枫, 王程斯, 黄安子, 樊龙. 轻量化输电线路缺陷检测方法[J]. 《计算机应用》唯一官方网站, 2026, 46(3): 969-979.

Figures/Tables 20

Fig. 1 Common component defect types in high-voltage transmission lines

Fig. 2 Overall structure of proposed method

Fig. 3 Structure of SepViT-DCN collaborative module

Fig. 4 Structure of BR-FPN module

Fig. 5 Structure of Light-YOLO

Fig. 6 Structure of SCDA module based on contrastive learning

Fig. 7 Structure of Dual-DifferNet

Fig. 8 Loss function curves and mAP curves on validation dataset

Fig. 9 Precision and recall analysis for all categories by Light-YOLO

Fig. 10 Confusion matrix

Tab. 1 Comparison of detection performance of transmission line components by different models

模型	精确率/%	召回率/%	mAP@50/%	mAP@［50：95］/%	浮点运算量/GFLOPs	参数量/10⁶
Faster R-CNN^［25］	56.6	53.4	59.2	51.5	134.38	41.76
SSD^［26］	67.2	64.6	70.1	62.8	34.86	35.64
I2D-Net^［7］	89.6	86.1	83.1	71.2	79.50	87.50
YOLOv3^［27］	88.7	89.1	91.6	74.3	283.10	103.70
YOLOv5	91.5	84.9	90.6	72.3	64.40	25.10
YOLOv8^［14］	80.4	76.5	80.8	64.7	69.10	24.90
YOLOv10^［28］	88.9	86.4	90.6	75.1	64.10	16.50
YOLOv11n^［29］	88.9	86.8	89.5	70.0	6.50	2.59
YOLOv11s	88.0	88.7	90.7	67.8	21.60	9.43
YOLOv11m	88.9	87.5	90.6	75.1	68.00	16.11
YOLOv11l	89.7	89.0	92.1	77.0	86.70	25.29
YOLOv11x	90.1	89.5	92.6	77.3	195.60	56.89
Light-YOLOx	98.2	95.2	97.4	87.6	120.50	27.00
Light-YOLO	97.4	91.5	96.9	85.3	29.90	11.24

Tab. 2 Effectiveness of each module in Light-YOLO model

实验组	模型配置	mAP@50/%	帧率/（frame·s^-1）
Baseline	YOLOv8	80.8	118
+SepViT	替换C2f为SepViT	87.2	105
+DCN	添加可变形卷积	92.1	103
+BR-FPN	替换FPN为BR-FPN	96.9	98

Tab. 3 Ablation experiment results

+SepViT@-1	+SepViT@-2	+SepViT@-3	浮点运算量/GFLOPs	mAP@50/%
√			23.5	87.4
√	√		26.7	89.3
√	√	√	29.7	91.5

Tab. 4 Impact of channel sparsity threshold on model performance

$γ$	mAP@50/%	浮点运算量/GFLOPs	参数量/10⁶
0.0	96.9	29.9	11.24
0.1	96.5	26.9	10.12
0.2	96.0	22.4	9.80
0.3	90.2	19.8	9.20
0.4	89.1	18.1	8.60

Tab. 4 Impact of channel sparsity threshold on model performance

$γ$	mAP@50/%	浮点运算量/GFLOPs	参数量/10⁶
0.0	96.9	29.9	11.24
0.1	96.5	26.9	10.12
0.2	96.0	22.4	9.80
0.3	90.2	19.8	9.20
0.4	89.1	18.1	8.60

Fig. 11 Visualization of feature maps corresponding to channel clipping under different thresholds

Fig. 12 Comparison of defect detection performance of different components

Tab. 5 Comparison of AUROC results on different transmission line components

类别	DifferNet	Dual-DifferNet
平均	88.2	93.5
架线悬挂	91.7	95.1
玻璃绝缘子	82.8	92.4
避雷针悬挂	93.0	97.0
聚合物绝缘子上拉环	86.4	89.0
可变张力夹	86.2	94.0

Tab. 6 Performance comparison under edge deployment

方法

Jetson NX

无人机

推理速度/

（frame·s^-1）

吞吐量/

（frame·min^-1）

Fig. 13 Detection results of key components of transmission lines

Fig. 14 Successfully identified components with defects

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Lightweight method for transmission line defect detection

轻量化输电线路缺陷检测方法

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