A Novel Detection Method Using YOLOv5 for Vehicle Target under Complex Situation Article Swipe
Yue Zhai
,
Weijia Zeng
,
Nan Li
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.18280/ts.390407
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.18280/ts.390407
Aiming at the problem of low accuracy of vehicle image detection and recognition caused by low visibility in foggy weather, an improved YOLOv5 algorithm is proposed. This algorithm adjusts the brightness and contrast of the image by adding the improved adaptive histogram equalization method to the image preprocessing, highlights the detailed information of vehicle image signs, and changes the backbone network standard convolution mode to the depth separable convolution method for model lightweight processing. By constructing the corresponding vehicle target detection data set, this paper is superior to the object detection model commonly used on the public data set in terms of performance and effectiveness, and draws the following conclusion from the comparison results of ablation experiments, the improved algorithm improves the detection accuracy of a single image and reduces the processing time.
Related Topics
Concepts
Computer science
Artificial intelligence
Computer vision
Preprocessor
Histogram equalization
Brightness
Visibility
Convolution (computer science)
Adaptive histogram equalization
Set (abstract data type)
Image (mathematics)
Object detection
Pattern recognition (psychology)
Histogram
Data set
Contrast (vision)
Artificial neural network
Optics
Physics
Programming language
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.18280/ts.390407
- https://www.iieta.org/download/file/fid/81596
- OA Status
- bronze
- Cited By
- 7
- References
- 14
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4298009709Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.18280/ts.390407Digital Object Identifier
- Title
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A Novel Detection Method Using YOLOv5 for Vehicle Target under Complex SituationWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
-
2022-08-31Full publication date if available
- Authors
-
Yue Zhai, Weijia Zeng, Nan LiList of authors in order
- Landing page
-
https://doi.org/10.18280/ts.390407Publisher landing page
- PDF URL
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https://www.iieta.org/download/file/fid/81596Direct link to full text PDF
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YesWhether a free full text is available
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bronzeOpen access status per OpenAlex
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https://www.iieta.org/download/file/fid/81596Direct OA link when available
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Computer science, Artificial intelligence, Computer vision, Preprocessor, Histogram equalization, Brightness, Visibility, Convolution (computer science), Adaptive histogram equalization, Set (abstract data type), Image (mathematics), Object detection, Pattern recognition (psychology), Histogram, Data set, Contrast (vision), Artificial neural network, Optics, Physics, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
7Total citation count in OpenAlex
- Citations by year (recent)
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2025: 3, 2024: 1, 2023: 3Per-year citation counts (last 5 years)
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14Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.to | 44, 64, 87 |
| abstract_inverted_index.and | 11, 31, 56, 103, 105, 128 |
| abstract_inverted_index.for | 70 |
| abstract_inverted_index.low | 5, 15 |
| abstract_inverted_index.set | 98 |
| abstract_inverted_index.the | 2, 29, 34, 38, 45, 49, 58, 65, 76, 88, 95, 107, 111, 117, 121, 130 |
| abstract_inverted_index.This | 26 |
| abstract_inverted_index.data | 81, 97 |
| abstract_inverted_index.from | 110 |
| abstract_inverted_index.mode | 63 |
| abstract_inverted_index.set, | 82 |
| abstract_inverted_index.this | 83 |
| abstract_inverted_index.used | 93 |
| abstract_inverted_index.depth | 66 |
| abstract_inverted_index.draws | 106 |
| abstract_inverted_index.foggy | 18 |
| abstract_inverted_index.image | 9, 35, 46, 54, 127 |
| abstract_inverted_index.model | 71, 91 |
| abstract_inverted_index.paper | 84 |
| abstract_inverted_index.terms | 100 |
| abstract_inverted_index.time. | 132 |
| abstract_inverted_index.Aiming | 0 |
| abstract_inverted_index.YOLOv5 | 22 |
| abstract_inverted_index.adding | 37 |
| abstract_inverted_index.caused | 13 |
| abstract_inverted_index.method | 43, 69 |
| abstract_inverted_index.object | 89 |
| abstract_inverted_index.public | 96 |
| abstract_inverted_index.signs, | 55 |
| abstract_inverted_index.single | 126 |
| abstract_inverted_index.target | 79 |
| abstract_inverted_index.adjusts | 28 |
| abstract_inverted_index.changes | 57 |
| abstract_inverted_index.network | 60 |
| abstract_inverted_index.problem | 3 |
| abstract_inverted_index.reduces | 129 |
| abstract_inverted_index.results | 113 |
| abstract_inverted_index.vehicle | 8, 53, 78 |
| abstract_inverted_index.ablation | 115 |
| abstract_inverted_index.accuracy | 6, 123 |
| abstract_inverted_index.adaptive | 40 |
| abstract_inverted_index.backbone | 59 |
| abstract_inverted_index.commonly | 92 |
| abstract_inverted_index.contrast | 32 |
| abstract_inverted_index.detailed | 50 |
| abstract_inverted_index.improved | 21, 39, 118 |
| abstract_inverted_index.improves | 120 |
| abstract_inverted_index.standard | 61 |
| abstract_inverted_index.superior | 86 |
| abstract_inverted_index.weather, | 19 |
| abstract_inverted_index.algorithm | 23, 27, 119 |
| abstract_inverted_index.detection | 10, 80, 90, 122 |
| abstract_inverted_index.following | 108 |
| abstract_inverted_index.histogram | 41 |
| abstract_inverted_index.proposed. | 25 |
| abstract_inverted_index.separable | 67 |
| abstract_inverted_index.brightness | 30 |
| abstract_inverted_index.comparison | 112 |
| abstract_inverted_index.conclusion | 109 |
| abstract_inverted_index.highlights | 48 |
| abstract_inverted_index.processing | 131 |
| abstract_inverted_index.visibility | 16 |
| abstract_inverted_index.convolution | 62, 68 |
| abstract_inverted_index.information | 51 |
| abstract_inverted_index.lightweight | 72 |
| abstract_inverted_index.performance | 102 |
| abstract_inverted_index.processing. | 73 |
| abstract_inverted_index.recognition | 12 |
| abstract_inverted_index.constructing | 75 |
| abstract_inverted_index.equalization | 42 |
| abstract_inverted_index.experiments, | 116 |
| abstract_inverted_index.corresponding | 77 |
| abstract_inverted_index.effectiveness, | 104 |
| abstract_inverted_index.preprocessing, | 47 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/11 |
| sustainable_development_goals[0].score | 0.6800000071525574 |
| sustainable_development_goals[0].display_name | Sustainable cities and communities |
| citation_normalized_percentile.value | 0.86484873 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |