Laser & Optoelectronics Progress, Volume. 59, Issue 24, 2410008(2022)

Insulator Defect Detection Based on Multi-Scale Feature Coding and Dual Attention Fusion

Lirong Li1,2、*, Peng Chen1, Yunliang Zhang1, Kai Zhang1, Wei Xiong1,2, and Pengcheng Gong1,2
Author Affiliations
  • 1School of Electrical and Electronic Engineering, Hubei University of Technology, Hubei 430064, Wuhan, China
  • 2Hubei Engineering Research Center of New Energy and Power Grid Equipment Safety Monitoring, Hubei 430064, Wuhan, China
  • show less
    References(29)

    [1] Park K C, Motai Y, Yoon J R. Acoustic fault detection technique for high-power insulators[J]. IEEE Transactions on Industrial Electronics, 64, 9699-9708(2017).

    [2] Lei X S, Sui Z H. Intelligent fault detection of high voltage line based on the Faster R-CNN[J]. Measurement, 138, 379-385(2019).

    [3] Liang H G, Zuo C, Wei W M. Detection and evaluation method of transmission line defects based on deep learning[J]. IEEE Access, 8, 38448-38458(2020).

    [4] Miao X R, Liu X Y, Chen J et al. Insulator detection in aerial images for transmission line inspection using single shot multibox detector[J]. IEEE Access, 7, 9945-9956(2019).

    [5] Zhang X Y, An J B, Chen F M. A simple method of tempered glass insulator recognition from airborne image[C], 127-130(2010).

    [6] Tan P, Li X F, Xu J M et al. Catenary insulator defect detection based on contour features and gray similarity matching[J]. Journal of Zhejiang University-SCIENCE A, 21, 64-73(2020).

    [7] Wu Q G, An J B. An active contour model based on texture distribution for extracting inhomogeneous insulators from aerial images[J]. IEEE Transactions on Geoscience and Remote Sensing, 52, 3613-3626(2014).

    [8] Tao X, Zhang D P, Wang Z H et al. Detection of power line insulator defects using aerial images analyzed with convolutional neural networks[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 50, 1486-1498(2020).

    [9] Wang B F, Zhao H T. Small object detection in hyperspectral images based on radial basis activation function[J]. Acta Optica Sinica, 41, 2311001(2021).

    [10] Liang X, Li J W, Zhao X L et al. Infrared target imaging liquid level detection method based on deep learning[J]. Acta Optica Sinica, 41, 2110001(2021).

    [11] Hu J, Liu H, Xu W C et al. Position detection algorithm of road obstacles based on 3D LiDAR[J]. Chinese Journal of Lasers, 48, 2410001(2021).

    [12] Jiang H, Qiu X J, Chen J et al. Insulator fault detection in aerial images based on ensemble learning with multi-level perception[J]. IEEE Access, 7, 61797-61810(2019).

    [13] Liu W, Anguelov D, Erhan D et al. SSD: single shot multibox detector[M]. Leibe B, Matas J, Sebe N, et al. Computer vision-ECCV 2016. Lecture notes in computer science, 9905, 21-37(2016).

    [15] 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, 39, 1137-1149(2017).

    [16] Liu J J, Liu C Y, Wu Y Q et al. An improved method based on deep learning for insulator fault detection in diverse aerial images[J]. Energies, 14, 4365(2021).

    [18] He K M, Zhang X Y, Ren S Q et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[J]. IEEE Transactions on Pattern Analysis Machine Intelligence, 37, 1904-1916(2015).

    [19] Wu X W, Sahoo D, Hoi S C H. Recent advances in deep learning for object detection[J]. Neurocomputing, 396, 39-64(2020).

    [20] Law H, Deng J. CornerNet: detecting objects as paired keypoints[J]. International Journal of Computer Vision, 128, 642-656(2020).

    [21] Zhou X Y, Zhuo J C, Krähenbühl P. Bottom-up object detection by grouping extreme and center points[C], 850-859(2019).

    [23] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition[C], 770-778(2016).

    [24] Bao W X, Yang Y P, Liang D et al. Multi-residual module stacked hourglass networks for human pose estimation[J]. Journal of Beijing Institute of Technology (English Edition), 29, 110-119(2020).

    [25] Feng Z Y, Jin L W, Tao D P et al. DLANet: a manifold-learning-based discriminative feature learning network for scene classification[J]. Neurocomputing, 157, 11-21(2015).

    [26] Gao S H, Cheng M M, Zhao K et al. Res2Net: a new multi-scale backbone architecture[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43, 652-662(2021).

    [27] Chen L C, Papandreou G, Kokkinos I et al. DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40, 834-848(2018).

    [28] Hu J, Shen L, Albanie S et al. Squeeze-and-excitation networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42, 2011-2023(2020).

    [29] Wang Q L, Wu B G, Zhu P F et al. ECA-net: efficient channel attention for deep convolutional neural networks[C], 11531-11539(2020).

    Tools

    Get Citation

    Copy Citation Text

    Lirong Li, Peng Chen, Yunliang Zhang, Kai Zhang, Wei Xiong, Pengcheng Gong. Insulator Defect Detection Based on Multi-Scale Feature Coding and Dual Attention Fusion[J]. Laser & Optoelectronics Progress, 2022, 59(24): 2410008

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Category: Image Processing

    Received: Sep. 28, 2021

    Accepted: Nov. 3, 2021

    Published Online: Jan. 6, 2023

    The Author Email: Li Lirong (Rongli@hbut.edu.cn)

    DOI:10.3788/LOP202259.2410008

    Topics