Distributed acoustic sensing signal event recognition and localization based on improved YOLOv7 Article Swipe
Hongyu Zhang
,
Chang Wang
,
Faxiang Zhang
,
Shaodong Jiang
,
Zhihui Sun
,
Xiaodong Wang
,
Zhenhui Duan
,
Fengxia Gao
,
Zhaoying Liu
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1088/1742-6596/2822/1/012085
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1088/1742-6596/2822/1/012085
The distributed acoustic sensing (DAS) system based on phase-sensitive optical time domain reflection ( Φ-OTDR) technology is widely used in pipeline safety monitoring, perimeter security, structure monitoring, etc. Accurate localization and recognition of multi-scene events over long distances has always been a challenge. This paper proposes an improved YOLOv7 algorithm for multi-event real-time detection of DAS system. The algorithm employs space-to-depth Conv(SPD-Conv) to replace the strided convolutions and pooling operations in YOLOv7, reducing fine-grained information loss and learning of inefficient feature representations. In addition, the Convolutional Block Attention Module (CBAM) is introduced in YOLOv7 to improve the model performance. We collected spatial–temporal signal data for six types of pipeline safety events, and passed them into the improved YOLOv7 algorithm in the form of data matrixes for training and evaluation. Experiments have shown that the proposed method achieves an [email protected] (mean Average Precision) of 99.7% for the identification of six pipeline safety event types. Positioning loss reduced to 0.2%, and detection speed can reach 70 frames per second(FPS). Our scheme achieves significant improvements in localization and classification accuracy compared to Faster R-CNN, etc. The event recognition localization method proposed in this paper has the advantage of fast speed and high accuracy.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1088/1742-6596/2822/1/012085
- OA Status
- diamond
- Cited By
- 1
- References
- 13
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4403837133
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4403837133Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1088/1742-6596/2822/1/012085Digital Object Identifier
- Title
-
Distributed acoustic sensing signal event recognition and localization based on improved YOLOv7Work title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-09-01Full publication date if available
- Authors
-
Hongyu Zhang, Chang Wang, Faxiang Zhang, Shaodong Jiang, Zhihui Sun, Xiaodong Wang, Zhenhui Duan, Fengxia Gao, Zhaoying LiuList of authors in order
- Landing page
-
https://doi.org/10.1088/1742-6596/2822/1/012085Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1088/1742-6596/2822/1/012085Direct OA link when available
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Event (particle physics), SIGNAL (programming language), Computer science, Speech recognition, Detection theory, Acoustic sensor, Acoustics, Pattern recognition (psychology), Artificial intelligence, Telecommunications, Physics, Detector, Quantum mechanics, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1Per-year citation counts (last 5 years)
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13Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.scheme | 169 |
| abstract_inverted_index.signal | 103 |
| abstract_inverted_index.system | 6 |
| abstract_inverted_index.types. | 153 |
| abstract_inverted_index.widely | 18 |
| abstract_inverted_index.Average | 141 |
| abstract_inverted_index.YOLOv7, | 72 |
| abstract_inverted_index.employs | 60 |
| abstract_inverted_index.events, | 111 |
| abstract_inverted_index.feature | 81 |
| abstract_inverted_index.improve | 96 |
| abstract_inverted_index.optical | 10 |
| abstract_inverted_index.pooling | 69 |
| abstract_inverted_index.reduced | 156 |
| abstract_inverted_index.replace | 64 |
| abstract_inverted_index.sensing | 4 |
| abstract_inverted_index.strided | 66 |
| abstract_inverted_index.system. | 57 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Accurate | 29 |
| abstract_inverted_index.accuracy | 177 |
| abstract_inverted_index.achieves | 137, 170 |
| abstract_inverted_index.acoustic | 3 |
| abstract_inverted_index.compared | 178 |
| abstract_inverted_index.improved | 48, 117 |
| abstract_inverted_index.learning | 78 |
| abstract_inverted_index.matrixes | 125 |
| abstract_inverted_index.pipeline | 21, 109, 150 |
| abstract_inverted_index.proposed | 135, 188 |
| abstract_inverted_index.proposes | 46 |
| abstract_inverted_index.reducing | 73 |
| abstract_inverted_index.training | 127 |
| abstract_inverted_index.Φ-OTDR) | 15 |
| abstract_inverted_index.Attention | 88 |
| abstract_inverted_index.accuracy. | 200 |
| abstract_inverted_index.addition, | 84 |
| abstract_inverted_index.advantage | 194 |
| abstract_inverted_index.algorithm | 50, 59, 119 |
| abstract_inverted_index.collected | 101 |
| abstract_inverted_index.detection | 54, 160 |
| abstract_inverted_index.distances | 38 |
| abstract_inverted_index.perimeter | 24 |
| abstract_inverted_index.real-time | 53 |
| abstract_inverted_index.security, | 25 |
| abstract_inverted_index.structure | 26 |
| abstract_inverted_index.Precision) | 142 |
| abstract_inverted_index.challenge. | 43 |
| abstract_inverted_index.introduced | 92 |
| abstract_inverted_index.operations | 70 |
| abstract_inverted_index.reflection | 13 |
| abstract_inverted_index.technology | 16 |
| abstract_inverted_index.Experiments | 130 |
| abstract_inverted_index.Positioning | 154 |
| abstract_inverted_index.distributed | 2 |
| abstract_inverted_index.evaluation. | 129 |
| abstract_inverted_index.inefficient | 80 |
| abstract_inverted_index.information | 75 |
| abstract_inverted_index.monitoring, | 23, 27 |
| abstract_inverted_index.multi-event | 52 |
| abstract_inverted_index.multi-scene | 34 |
| abstract_inverted_index.recognition | 32, 185 |
| abstract_inverted_index.significant | 171 |
| abstract_inverted_index.convolutions | 67 |
| abstract_inverted_index.fine-grained | 74 |
| abstract_inverted_index.improvements | 172 |
| abstract_inverted_index.localization | 30, 174, 186 |
| abstract_inverted_index.performance. | 99 |
| abstract_inverted_index.second(FPS). | 167 |
| abstract_inverted_index.Convolutional | 86 |
| abstract_inverted_index.Conv(SPD-Conv) | 62 |
| abstract_inverted_index.classification | 176 |
| abstract_inverted_index.identification | 147 |
| abstract_inverted_index.space-to-depth | 61 |
| abstract_inverted_index.phase-sensitive | 9 |
| abstract_inverted_index.representations. | 82 |
| abstract_inverted_index.spatial–temporal | 102 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 9 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/7 |
| sustainable_development_goals[0].score | 0.5799999833106995 |
| sustainable_development_goals[0].display_name | Affordable and clean energy |
| citation_normalized_percentile.value | 0.65315338 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |