面向变电设备金属锈蚀检测的分层嵌套标注方法

Translated title of the contribution: Hierarchical annotation method for metal corrosion detection of power equipment

Baili Zhang, Yong Cao, Pei Zhang, Zhao Zhang, Yina He, Mingjun Zhong

Research output: Contribution to journalArticlepeer-review

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.

Translated title of the contributionHierarchical annotation method for metal corrosion detection of power equipment
Original languageChinese (Traditional)
Pages (from-to)350-355
Number of pages6
JournalJournal of Southeast University (English Edition)
Volume37
Issue number4
DOIs
Publication statusPublished - 1 Dec 2021

Bibliographical note

Funding Information:
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).

Keywords

  • Deep learning
  • Faster R-CNN
  • Hierarchical annotation
  • Object detection
  • YOLOv5

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