PAS-YOLO: improved YOLOv8 integrating parameter-free attention and channel shuffle for object detection of power grid equipment Article Swipe
Meng Xu
,
Baofeng Li
,
Junchi Su
,
Yu Qin
,
Qiangwei Li
,
Jiansheng Lu
,
Zhiheng Shi
,
Xin Gao
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1088/1742-6596/2835/1/012034
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1088/1742-6596/2835/1/012034
Using an object detection network to identify power grid equipment in low-voltage stations automatically can effectively improve the accuracy and efficiency of information inspection, which is of great significance to the safe and stable operation of a power system. The current advanced object detection model YOLOv8 has high accuracy and efficiency in detection tasks. However, it still suffers from leakage and misclassification in complex scenarios, and there are higher lightweighting requirements in the face of real-time detection processes. This paper proposes a lightweight power grid equipment object detection model, PAS-YOLO. It uses a parameter-free attention module to extract features through convolution. A ShuffleNet Block, which consists of multiple channel shuffle modules, is introduced to reduce the convolution kernel parameters and improve computational efficiency. Extensive experiments on several benchmark and power grid equipment datasets demonstrate that our method outperforms the original YOLOv8 on multiple object detection metrics.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1088/1742-6596/2835/1/012034
- OA Status
- diamond
- Cited By
- 2
- References
- 8
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4402515076
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4402515076Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1088/1742-6596/2835/1/012034Digital Object Identifier
- Title
-
PAS-YOLO: improved YOLOv8 integrating parameter-free attention and channel shuffle for object detection of power grid equipmentWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-08-01Full publication date if available
- Authors
-
Meng Xu, Baofeng Li, Junchi Su, Yu Qin, Qiangwei Li, Jiansheng Lu, Zhiheng Shi, Xin GaoList of authors in order
- Landing page
-
https://doi.org/10.1088/1742-6596/2835/1/012034Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1088/1742-6596/2835/1/012034Direct OA link when available
- Concepts
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Power grid, Grid, Channel (broadcasting), Object (grammar), Computer science, Power (physics), Electrical engineering, Telecommunications, Artificial intelligence, Engineering, Physics, Mathematics, Geometry, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 1Per-year citation counts (last 5 years)
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8Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.scenarios, | 65 |
| abstract_inverted_index.convolution | 117 |
| abstract_inverted_index.demonstrate | 134 |
| abstract_inverted_index.effectively | 16 |
| abstract_inverted_index.efficiency. | 123 |
| abstract_inverted_index.experiments | 125 |
| abstract_inverted_index.information | 23 |
| abstract_inverted_index.inspection, | 24 |
| abstract_inverted_index.lightweight | 83 |
| abstract_inverted_index.low-voltage | 12 |
| abstract_inverted_index.outperforms | 138 |
| abstract_inverted_index.convolution. | 101 |
| abstract_inverted_index.requirements | 71 |
| abstract_inverted_index.significance | 29 |
| abstract_inverted_index.automatically | 14 |
| abstract_inverted_index.computational | 122 |
| abstract_inverted_index.lightweighting | 70 |
| abstract_inverted_index.parameter-free | 94 |
| abstract_inverted_index.misclassification | 62 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 8 |
| citation_normalized_percentile.value | 0.71931274 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |