[1] Zhang Baili, Cao Yong, Zhang Pei, Zhang Zhao, et al. Hierarchical annotation method for metal corrosiondetection of power equipment [J]. Journal of Southeast University (English Edition), 2021, 37 (4): 350-355. [doi:10.3969/j.issn.1003-7985.2021.04.002]

Copy

Hierarchical annotation method for metal corrosiondetection of power equipment()

Share：

Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

37

Issue:

2021 4

Page:

350-355

Research Field:

Automation

Publishing date:

2021-12-20

Info

Title:

Hierarchical annotation method for metal corrosiondetection of power equipment

Author(s):

Zhang Baili¹;  Cao Yong¹;  Zhang Pei²;  3;  Zhang Zhao²;  3;  He Yina¹;  Zhong Mingjun⁴

¹School of Computer Science and Engineering, Southeast University, Nanjing 211189, China
²State Key Laboratory of Smart Grid Protection and Control, State Grid Electric Power Research Institute, Nanjing 211106, China
³NARI Group Corporation, Nanjing 211106, China
⁴Department of Computing Science, University of Aberdeen, Aberdeen AB24 3UE, UK

Keywords:

deep learning;  Faster R-CNN;  YOLOv5;  object detection;  hierarchical annotation

PACS:

TP181

DOI:

10.3969/j.issn.1003-7985.2021.04.002

Abstract:

To solve the ambiguity and uncertainty in the labeling process of power equipment corrosion datasets, a novel hierarchical annotation method(HAM)is proposed. Firstly, large boxes are used to label a large area covering the range of corrosion, provided that the area is visually continuous and adjacent to corrosion that cannot be clearly divided. Secondly, in each labeling box established in the first step, regions with distinct corrosion and relative independence are labeled to form a second layer of nested boxes. Finally, a series of comparative experiments are conducted with other common annotation methods to validate the effectiveness of HAM. The experimental results show that, with the help of HAM, the recall of YOLOv5 increases from 50.79% to 59.41%; the recall of Faster R-CNN+VGG16 increases from 66.50% to 78.94%; the recall of Faster R-CNN+Res101 increases from 78.32% to 84.61%. Therefore, HAM can effectively improve the detection ability of mainstream models in detecting metal corrosion.

References:

[1] Roberge P R. Handbook of corrosion engineering[M]. New York, USA: McGraw-Hill, 2019:25-61.
[2] Volkan C. Corrosion engineering[M]. Beverly: Scrivener Publishing LLC, 2014:1-19.
[3] Dunn W L, Yacout A M. Corrosion detection in aircraft by X-ray backscatter methods[J]. Applied Radiation and Isotopes, 2000, 53(4): 625-632.DOI: 10.1016/S0969-8043(00)00240-2.
[4] Gao T, Sun H, Hong Y. Hidden corrosion detection using laser ultrasonic guided waves with multi-frequency local wavenumber estimation[J]. Ultrasonics, 2020, 108: 106182. DOI: 10.1016/j.ultras.2020.106182.
[5] Doshvarpassand S, Wu C, Wang X. An overview of corrosion defect characterization using active infrared thermography[J]. Infrared Physics & Technology, 2019, 96: 366-389. DOI: 10.1016/j.infrared.2018.12.006.
[6] Wicker M, Alduse B P, Jung S. Detection of hidden corrosion in metal roofing shingles utilizing infrared thermography[J]. Journal of Building Engineering, 2018, 20: 201-207. DOI: 10.1016/j.jobe.2018.07.018.
[7] Dudziak M J, Chervonenkis A Y, Chinarov V. Nondestructive evaluation for crack, corrosion, and stress detection for metal assemblies and structures[C]//Nondestructive Evaluation of Aging Aircraft, Airports, and Aerospace Hardware Ⅲ. International Society for Optics and Photonics, Newport Beach, CA, USA, 1999, 3586: 20-31. DOI: 10.1117/12.339888.
[8] LeCun Y, Boser B, Denker J S. Backpropagation applied to handwritten zip code recognition[J]. Neural computation, 1989, 1(4): 541-551. DOI: 10.1162/neco.1989.1.4.541.
[9] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 2017, 60: 84-90. DOI: 10.1145/3065386.
[10] Ren S, He K, Girshick R. Faster R-CNN: Towards real-time object detection with region proposal networks[J]. IEEE transactions on pattern analysis and machine intelligence, 2016, 39(6): 1137-1149. DOI: 10.1109/TPAMI.2016.2577031.
[11] Redmon J, Divvala S, Girshick R. You only look once: Unified, real-time object detection[C]// IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, NV, USA, 2016: 779-788. DOI: 10.1109/CVPR.2016.91.
[12] Redmon J, Farhadi A. YOLO9000: Better, faster, stronger[C]// IEEE Conference on Computer Vision and Pattern Recognition.Honolulu, Hawaii, US, 2017: 6517-6525, doi: 10.1109/CVPR.2017.690.
[13] Fan H B, Hu X X, Liu Y M. Application of deep learning in corrosion detection of power equipment[J]. Guangdong Electric Power, 2020, 33(9): 154-165. DOI: 10.3969/j.issn.1007-290X.2020.009.020.( in Chinese)
[14] Yao Y, Yang Y, Wang Y. Artificial intelligence-based hull structural plate corrosion damage detection and recognition using convolutional neural network[J]. Applied Ocean Research, 2019, 90: 101823. DOI: 10.1016/j.apor.2019.05.008.
[15] Glenn J, Alex S, Jirka B.Ultralytics/yolov5: v3.1-bug fixes and performance improvements[EB/OL].(2020-10-29)[2021-02-01]. https://zenodo.org/record/4154370#.YT4F4nbJ3Us. DOI:10.5281/zenodo.4154370.
[16] He K, Zhang X, Ren S. Deep residual learning for image recognition[C]// IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, NV, USA, 2016: 770-778.
[17] Sutskever I, Martens J, Dahl G. On the importance of initialization and momentum in deep learning[C]//International Conference on Machine Learning. Atlanta, USA, 2013: 1139-1147.
[18] Ioffe S, Szegedy C. Batch normalization: Accelerating deep network training by reducing internal covariate shift[C]//International Conference on Machine Learning. Stroudsburg, PA, USA, 2015, 37: 448-456. DOI: 10.5555/3045118.3045167.
[19] Nair V, Hinton G E. Rectified linear units improve restricted boltzmann machines[C]// International Conference on International Conference on Machine Learning. Haifa, Israel, 2010: 807-814.

Memo

Memo:

Biography: Zhang Baili(1970—), male, doctor, associate professor, 402468432@qq.com.
Foundation items: The National Key R&D Program of China(No. 2018YFC0830200), the Open Research Fund from State Key Laboratory of Smart Grid Protection and Control(No. NARI-T-2-2019189), Rapid Support Project(No. 61406190120), the Fundamental Research Funds for the Central Universities(No. 2242021k10011).
Citation: Zhang Baili, Cao Yong, Zhang Pei, et al.Hierarchical annotation method for metal corrosion detection of power equipment[J].Journal of Southeast University(English Edition), 2021, 37(4):350-355.DOI:10.3969/j.issn.1003-7985.2021.04.002.

Common functions