Computer Science

Computer Science ›› 2026, Vol. 53 ›› Issue (6A): 250400077-7.doi: 10.11896/jsjkx.250400077

• Image Processing & Multimedia Technology • Previous Articles     Next Articles

Infrared and Visible Image Homography Estimation for Power Equipment Based on Improved MobileNetV4

WANG Sheng1,2, ZHANG Linghao1,2, ZHANG Juling1,2, PANG Bo1,2, XI Ning3, SHE Wenkui4   

  1. 1 State Grid Sichuan Electric Power Research Institute,Chengdu 610043,China
    2 Power System Security and Operation Key Laboratory of Sichuan Province,Chengdu 610043,China
    3 State Grid Tianfu New Area Power Supply Company,Chengdu 610200,China
    4 Aostar Information Technology Co.,Ltd.,Chengdu 610200,China
  • Online:2026-06-16 Published:2026-06-12
  • About author:WANG Sheng,born in 1987,master,senior engineer.His main research interests include power grid network security attack and defense technology,data security,Internet of things security,and industrial control security.
    SHE Wenkui,born in 1982,master,se-nior engineer.His main research in-terests include cloud computing,power Internet of Things.
  • Supported by:
    Scientific Research Foundation of State Grid Sichuan Electric Power Company(52199723002P).

PDF (PC)

20

Abstract: The homography estimation of infrared and visible images is one of the key techniques to improve the positioning accuracy and defect detection accuracy of power equipment.To address the problems of insufficient accuracy and large model size of existing methods in homography estimation of infrared and visible images of power equipment,a lightweight homography estimation method based on improved MobileNetV4 is proposed.Firstly,MobileNet is applied to the homography estimation task for the first time,and a lightweight estimation model is designed.Secondly,an improved MobileNetV4 model,CBMobileNet,is proposed by highlighting the key features in the feature map through the introduction of the CBAM module in each stage of MobileNetV4.Finally,the number of parameters and computational complexity of the model is significantly reduced using the L1 Norm pruning algorithm while ensuring less performance loss.The experimental results show that the average corner error of the proposed method substantially decreases from 5.06 to 4.95 compared to the suboptimal algorithm on the synthetic benchmark dataset.In addition,compared to the original model,the pruned model significantly reduces the parameters from 10.04 MB to 6.91 MB and the FLOPs from 1 029.48 MB to 755.11 MB,while the average corner error only slightly increases from 4.93 to 4.95.

Key words: Homography estimation, MobileNetV4, Model pruning, Infrared and visible image, Power equipment

CLC Number: 

  • TP391.41
[1] FU T,DENG C Z,HAN X Y,et al.Infrared and Visible Image Registration for Power Equipments Based on Deep Learnin[J].Infrared Technolog,2022,44(9):936-943.
[2] WEN S Y,ZHANG S,ZHANG C Y,et al.Multi-scale object detection algorithm based on improved YOLOv8[J].Modern Electronics Techniqu,2024,47(15):133-138.
[3] RAHMAN E U,ZHANG Y,AHMAD S,et al.Autonomous vision-based primary distribution systems porcelain insulators inspection using UAVs[J].Sensors,2021,21(3):974.
[4] LIN Y,LIU M,BAI D M,et al.Infrared-visible Image Smart Registration for Electrical Equipment Based on Deep Learning [J].Shandong Electric Power,2022,49(8):22-27.
[5] WANG X,LUO Y,FU Q,et al.Coarse-to-Fine HomographyEstimation for Infrared and Visible Images[J].Electronics,2023,12(21):4441.
[6] LOWE D G.Distinctive image features from scale-invariant keypoints[J].International journal of computer vision,2004,60:91-110.
[7] BAY H,TUYTELAARS T,VAN GOOL L.Surf:Speeded up robust features[C]//Computer Vision－ECCV 2006:9th European Conference on Computer Vision.Springer,2006:404-417.
[8] RUBLEE E,RABAUD V,KONOLIGE K,et al.ORB:An efficient alternative to SIFT or SURF[C]//2011 International conference on computer vision.IEEE,2011:2564-2571.
[9] WANG X Y,LUO Y H,WU Y Z,et al.Homography Estimation of Infrared and Visible Images Based on Unet[J].Aeronautical Computing Technique,2023,53(4):51-55.
[10] WANG X Y.Research on Homography Estimation for Infra-redand Visible Images based on Deep Learning [D].Guanghan:Civil Aviation Flight University of China,2024.
[11] LUO Y,WANG X,WU Y,et al.Detail-Aware Deep Homography Estimation for Infrared and Visible Image[J].Electronics,2022,11(24):4185.
[12] LUO Y,WANG X,WU Y,et al.Infrared and visible image homography estimation using multiscale generative adversarial network[J].Electronics,2023,12(4):788.
[13] WANG X,LUO Y,FU Q,et al.Infrared and Visible Image Homography Estimation Based on Feature Correlation Transformers for Enhanced 6G Space-Air-Ground Integrated Network Perception[J].Remote Sensing,2023,15(14):3535.
[14] QIN D,LEICHNER C,DELAKIS M,et al.MobileNetV4-Universal Models for the Mobile Ecosystem[J].arXiv:240410518,2024.
[15] WOO S,PARK J,LEE J Y,et al.Cbam:Convolutional block attention module[C]//Proceedings of the European conference on computer vision(ECCV).2018:3-19.
[16] LI H,KADAV A,DURDANOVIC I,et al.Pruning filters for efficient convnets[J].arXiv:160808710,2016.
[17] GKRISPANIS K,GKALELIS N,MEZARIS V.Filter-Pruningof Lightweight Face Detectors Using a Geometric Median Criterion[C]//Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision.2024:280-289.
[18] JIANG Q,LIU Y,YAN Y,et al.A contour angle orientation for power equipment infrared and visible image registration[J].IEEE Transactions on Power Delivery,2020,36(4):2559-2569.
[19] ZHANG J,WANG C,LIU S,et al.Content-aware unsupervised deep homography estimation[C]//Computer Vision－ECCV 2020:16th European Conference.Springer,2020:653-669.
[1] JI Sai, QIAO Liwei, SUN Yajie. Semantic-guided Hybrid Cross-feature Fusion Method for Infrared and Visible Light Images [J]. Computer Science, 2026, 53(2): 253-263.
[2] DING Zhengze, NIE Rencan, LI Jintao, SU Huaping, XU Hang. MTFuse:An Infrared and Visible Image Fusion Network Based on Mamba and Transformer [J]. Computer Science, 2025, 52(8): 188-194.
[3] WU Zhihua, CHENG Jianghua, LIU Tong, CAI Yahui, CHENG Bang, PAN Lehao. Human Target Detection Algorithm for Low-quality Laser Through-window Imaging [J]. Computer Science, 2025, 52(6A): 240600069-6.
[4] HUANG Haixin, XU Chenglong, FU Yao. Research on Structured Pruning Algorithm Based on Information Fusion [J]. Computer Science, 2025, 52(11A): 241000041-6.
[5] GAO Yang, CAO Yangjie, DUAN Pengsong. Lightweighting Methods for Neural Network Models:A Review [J]. Computer Science, 2024, 51(6A): 230600137-11.
[6] LI Dongyang, NIE Rencan, PAN Linna, LI He. UMGN:An Infrared and Visible Image Fusion Network Based on Unsupervised Significance MaskGuidance [J]. Computer Science, 2024, 51(6A): 230600170-5.
[7] . Research on Data Storage for Smart Grid Condition Monitoring Using Hadoop [J]. Computer Science, 2013, 40(1): 81-84.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
Add:18# Honghu West Rd., Yubei ,Chongqing,China    Post Code: 401121
Tel: 023-63500828/023-67039612/023-67039623
E-mail: jsjkx12@163.com
Powered by Beijing Magtech Co., Ltd.