MSIS: Multispectral Instance Segmentation Method for Power Equipment

Infrared image of power equipment is widely used in power equipment fault detection, and segmentation of infrared images is an important step in power equipment thermal fault detection. Nevertheless, since the overlap of the equipment, the complex background, and the low contrast of the infrared ima...

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Bibliographic Details
Published in:Computational intelligence and neuroscience 2022-01, Vol.2022, p.2864717-13
Main Authors: Shu, Jun, He, Juncheng, Li, Ling
Format: Article
Language:English
Subjects:
Algorithms
Clustering
Deep learning
Fault detection
Feature extraction
Image contrast
Image processing
Image segmentation
Infrared imagery
Instance segmentation
Machine learning
Modules
Neural networks
Semantics
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Summary:Infrared image of power equipment is widely used in power equipment fault detection, and segmentation of infrared images is an important step in power equipment thermal fault detection. Nevertheless, since the overlap of the equipment, the complex background, and the low contrast of the infrared image, the current method still cannot complete the detection and segmentation of the power equipment well. To better segment the power equipment in the infrared image, in this paper, a multispectral instance segmentation (MSIS) based on SOLOv2 is designed, which is an end-to-end and single-stage network. First, we provide a novel structure of multispectral feature extraction, which can simultaneously obtain rich features in visible images and infrared images. Secondly, a module of feature fusion (MARFN) has been constructed to fully obtain fusion features. Finally, the combination of multispectral feature extraction, the module of feature fusion (MARFN), and instance segmentation (SOLOv2) realize multispectral instance segmentation of power equipment. The experimental results show that the proposed MSIS model has an excellent performance in the instance segmentation of power equipment. The MSIS based on ResNet-50 has 40.06% AP.
ISSN:1687-5265
1687-5273
DOI:10.1155/2022/2864717