Electrical Thermal Image Semantic Segmentation: Large-scale Dataset and Baseline

The fault diagnosis of electrical equipment plays a vital role in the safe operation of the power system. The task of electrical thermal image semantic segmentation is to segment all electrical equipment in thermal images, which is a key step in automatic fault diagnosis of electrical equipment. How...

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Bibliographic Details
Published in:IEEE transactions on instrumentation and measurement 2022, Vol.71, p.1-1
Main Authors: Wang, Futian, Guo, Yin, Li, Chenglong, Lu, Andong, Ding, Zhongfeng, Tang, Jin, Luo, Bin
Format: Article
Language:English
Subjects:
Algorithms
Annotations
Benchmark dataset
Datasets
Edge extraction
Electric equipment
Electrical equipment
Fault diagnosis
Image annotation
Image edge detection
Image segmentation
Instruments
Performance evaluation
Power system stability
Semantic segmentation
Semantics
Sonar
Task analysis
Thermal image
Thermal imaging
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Summary:The fault diagnosis of electrical equipment plays a vital role in the safe operation of the power system. The task of electrical thermal image semantic segmentation is to segment all electrical equipment in thermal images, which is a key step in automatic fault diagnosis of electrical equipment. However, there lacks a large-scale dataset in this research field. Therefore, we contribute a large-scale dataset for electrical thermal image semantic segmentation. It contains 4839 thermal images and 17 types of electrical equipment. We provide pixel-level annotations to facilitate performance evaluation of different semantic segmentation algorithms. To provide a strong baseline, we propose a Cross Guidance Network (CGNet), which jointly infers semantic segmentation maps and edge extraction results in an end-to-end learning framework, for electrical thermal image semantic segmentation. Extensive experiments on our launched dataset demonstrate the effectiveness of the proposed CGNet, and it also achieves the best performance on the general thermal image segmentation dataset. We will release our code and dataset at https://github.com/guo49/CGNet-pytorch.
ISSN:0018-9456
1557-9662
DOI:10.1109/TIM.2022.3214605