Research on multi-branch residual connection spectrum image classification based on attention mechanism
Abstract The acoustic spectrogram arranges the frequencies in the sound along the frequency spread, and translates the spectral changes into the intensity, wavelength and frequency of the electrical signals. Currently, the extensive use of convolutional neural networks for spectral image classificat...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-11283-5 |
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| _version_ | 1849332999095582720 |
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| author | Zhong Xiaohui Dong Sheng Zhang Yiyi Lu Wei Jiang Lincen |
| author_facet | Zhong Xiaohui Dong Sheng Zhang Yiyi Lu Wei Jiang Lincen |
| author_sort | Zhong Xiaohui |
| collection | DOAJ |
| description | Abstract The acoustic spectrogram arranges the frequencies in the sound along the frequency spread, and translates the spectral changes into the intensity, wavelength and frequency of the electrical signals. Currently, the extensive use of convolutional neural networks for spectral image classification can extract signal features in the spectrogram, but the redundancy of noisy data generated by a large number of bands of the spectrum affects the feature information at different levels of the image. In order to optimize this problem, this paper proposes a multi-branch residual-connected Efficient Global Attention (EGA) acoustic spectral image classification network based on the attention mechanism, which firstly separates the components with their respective acoustic features from the spectral noise, so as to achieve the purpose of noise reduction, and then extracts the Phase Resolved Partial Discharge (PRPD) Spectrum of the Intermediate Frequency (IF) cycle for the original signals that have undergone noise reduction, which is based on the attention mechanism through the Improved Global Attention Mechanism (IGAM) in the EGA of the backbone network. mechanism pays more attention to the channel and spatial features of the spectrogram, then improves the feature extraction ability by residual connection, and finally performs feature fusion with the mask branch. The results show that a more accurate detection of abnormal partial discharge type of carbon brushes in gantry cranes is made, and the feasibility and innovativeness of the method is verified through experiments and production use. |
| format | Article |
| id | doaj-art-e1ee336b6be840f2acbfd947263b9edc |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-e1ee336b6be840f2acbfd947263b9edc2025-08-20T03:46:01ZengNature PortfolioScientific Reports2045-23222025-07-0115111110.1038/s41598-025-11283-5Research on multi-branch residual connection spectrum image classification based on attention mechanismZhong Xiaohui0Dong Sheng1Zhang Yiyi2Lu Wei3Jiang Lincen4Ningbo Beilun Third Container Terminal Co., LTDNingbo Beilun Third Container Terminal Co., LTDNingbo Beilun Third Container Terminal Co., LTDNingbo Beilun Third Container Terminal Co., LTDSchool of Communication and Information Engineering, Nanjing University of Posts and TelecommunicationsAbstract The acoustic spectrogram arranges the frequencies in the sound along the frequency spread, and translates the spectral changes into the intensity, wavelength and frequency of the electrical signals. Currently, the extensive use of convolutional neural networks for spectral image classification can extract signal features in the spectrogram, but the redundancy of noisy data generated by a large number of bands of the spectrum affects the feature information at different levels of the image. In order to optimize this problem, this paper proposes a multi-branch residual-connected Efficient Global Attention (EGA) acoustic spectral image classification network based on the attention mechanism, which firstly separates the components with their respective acoustic features from the spectral noise, so as to achieve the purpose of noise reduction, and then extracts the Phase Resolved Partial Discharge (PRPD) Spectrum of the Intermediate Frequency (IF) cycle for the original signals that have undergone noise reduction, which is based on the attention mechanism through the Improved Global Attention Mechanism (IGAM) in the EGA of the backbone network. mechanism pays more attention to the channel and spatial features of the spectrogram, then improves the feature extraction ability by residual connection, and finally performs feature fusion with the mask branch. The results show that a more accurate detection of abnormal partial discharge type of carbon brushes in gantry cranes is made, and the feasibility and innovativeness of the method is verified through experiments and production use.https://doi.org/10.1038/s41598-025-11283-5Image classificationAttention mechanismResidual connectionEGAFeature fusion |
| spellingShingle | Zhong Xiaohui Dong Sheng Zhang Yiyi Lu Wei Jiang Lincen Research on multi-branch residual connection spectrum image classification based on attention mechanism Scientific Reports Image classification Attention mechanism Residual connection EGA Feature fusion |
| title | Research on multi-branch residual connection spectrum image classification based on attention mechanism |
| title_full | Research on multi-branch residual connection spectrum image classification based on attention mechanism |
| title_fullStr | Research on multi-branch residual connection spectrum image classification based on attention mechanism |
| title_full_unstemmed | Research on multi-branch residual connection spectrum image classification based on attention mechanism |
| title_short | Research on multi-branch residual connection spectrum image classification based on attention mechanism |
| title_sort | research on multi branch residual connection spectrum image classification based on attention mechanism |
| topic | Image classification Attention mechanism Residual connection EGA Feature fusion |
| url | https://doi.org/10.1038/s41598-025-11283-5 |
| work_keys_str_mv | AT zhongxiaohui researchonmultibranchresidualconnectionspectrumimageclassificationbasedonattentionmechanism AT dongsheng researchonmultibranchresidualconnectionspectrumimageclassificationbasedonattentionmechanism AT zhangyiyi researchonmultibranchresidualconnectionspectrumimageclassificationbasedonattentionmechanism AT luwei researchonmultibranchresidualconnectionspectrumimageclassificationbasedonattentionmechanism AT jianglincen researchonmultibranchresidualconnectionspectrumimageclassificationbasedonattentionmechanism |