Optimized Non-Maximum Suppression for Multi-Source Local Discharge PRPD Pattern Identification Article Swipe
Yizhi Fang
,
Yuling Lin
,
Tianshu Li
,
Zicong Qiu
,
Yu-Hua Huang
,
Yi Xiao
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3233/faia241329
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3233/faia241329
The multi-source partial discharge PRPD pattern can realize the pattern recognition of multi-source partial discharge types through the target detection algorithm training and identification of shape features. However, when the characteristics of different discharge pattern overlap, the small target is easily blocked by the large target, resulting in false detection and missed detection. Therefore, this paper proposes a multi-source partial discharge PRPD pattern identification algorithm with optimized non-maximum suppression. The Soft-NMS algorithm was introduced to solve the missed detection caused by overlapping targets; GIoU was used to replace the traditional IoU to calculate the similarity between targets and the loss function was optimized; the YOLOv7 network model was further built to identify the PRPD pattern of four typical discharges shape features. After cross-validation between simulation experiments and charged field data, the results prove that the average detection accuracy of the algorithm can reach 98.2% in simulation experiments and 88.4% in field experiments, effectively reducing the false detection rate and successfully identifying the characteristics of multi-source local discharge PRPD pattern when the targets overlap.
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- Type
- book-chapter
- Language
- en
- Landing Page
- https://doi.org/10.3233/faia241329
- https://ebooks.iospress.nl/pdf/doi/10.3233/FAIA241329
- OA Status
- hybrid
- References
- 19
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4405377563
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Raw OpenAlex JSON
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https://openalex.org/W4405377563Canonical identifier for this work in OpenAlex
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https://doi.org/10.3233/faia241329Digital Object Identifier
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Optimized Non-Maximum Suppression for Multi-Source Local Discharge PRPD Pattern IdentificationWork title
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book-chapterOpenAlex work type
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enPrimary language
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2024Year of publication
- Publication date
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2024-12-13Full publication date if available
- Authors
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Yizhi Fang, Yuling Lin, Tianshu Li, Zicong Qiu, Yu-Hua Huang, Yi XiaoList of authors in order
- Landing page
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https://doi.org/10.3233/faia241329Publisher landing page
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https://ebooks.iospress.nl/pdf/doi/10.3233/FAIA241329Direct link to full text PDF
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://ebooks.iospress.nl/pdf/doi/10.3233/FAIA241329Direct OA link when available
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Identification (biology), Materials science, Environmental science, Biology, BotanyTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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19Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.features. | 26, 120 |
| abstract_inverted_index.optimized | 66 |
| abstract_inverted_index.resulting | 46 |
| abstract_inverted_index.Therefore, | 53 |
| abstract_inverted_index.detection. | 52 |
| abstract_inverted_index.discharges | 118 |
| abstract_inverted_index.introduced | 73 |
| abstract_inverted_index.optimized; | 102 |
| abstract_inverted_index.similarity | 94 |
| abstract_inverted_index.simulation | 124, 145 |
| abstract_inverted_index.effectively | 152 |
| abstract_inverted_index.experiments | 125, 146 |
| abstract_inverted_index.identifying | 160 |
| abstract_inverted_index.non-maximum | 67 |
| abstract_inverted_index.overlapping | 81 |
| abstract_inverted_index.recognition | 10 |
| abstract_inverted_index.traditional | 89 |
| abstract_inverted_index.experiments, | 151 |
| abstract_inverted_index.multi-source | 1, 12, 58, 164 |
| abstract_inverted_index.successfully | 159 |
| abstract_inverted_index.suppression. | 68 |
| abstract_inverted_index.identification | 23, 63 |
| abstract_inverted_index.characteristics | 30, 162 |
| abstract_inverted_index.cross-validation | 122 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile.value | 0.59571876 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |