Detection and ranging of small targets on water based on binocular camera and improved YOLOv5 algorithm Article Swipe
Yongguo Li
,
Caiyin Xu
,
Can Qin
,
Xiangyan Li
,
Xuan Tang
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-2381460/v1
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-2381460/v1
In order to meet the needs of intelligent ships to capture and grasp small targets while navigating on water and to be able to sense and avoid small targets, a water target detection method based on the YOLOv5-s algorithm is proposed, and the experimental equipment is allowed to perform real-time and high-precision target recognition and sensing in a dynamic water environment. This method uses the view acquisition by using the ZED 2i binocular camera, and the RGB images obtained by the binocular camera are used as the input of the feature fusion module to improve the YOLOv5-s algorithm to obtain the position information of the small target on water in the acquired image, and the relative position about the camera is calculated by combining the pixel position information obtained by the binocular camera. At the same time, in order to ensure the detection accuracy, the sample anchor frames of some datasets are updated according to the detection results; then the dataset is retrained; the images acquired through the binocular camera are corrected for distortion and stereo correction, etc., to make the detection accuracy higher; the experimental results show that the detection accuracy of combining the binocular camera with the improved YOLOv5-s algorithm is better than that of other methods for small target detection on water and better than the original algorithm The evaluation index map_0.5 is as high as 9.79%, and the detection accuracy error of the lateral detection of the target on water is kept at about 6.6%, and the lateral detection accuracy within 20 meters is about 8.7%. The obtained results can reliably provide a valuable basis for autonomous intelligent ships to work in complex water environment.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-2381460/v1
- https://www.researchsquare.com/article/rs-2381460/latest.pdf
- OA Status
- green
- References
- 10
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4313448644
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4313448644Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.21203/rs.3.rs-2381460/v1Digital Object Identifier
- Title
-
Detection and ranging of small targets on water based on binocular camera and improved YOLOv5 algorithmWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-12-20Full publication date if available
- Authors
-
Yongguo Li, Caiyin Xu, Can Qin, Xiangyan Li, Xuan TangList of authors in order
- Landing page
-
https://doi.org/10.21203/rs.3.rs-2381460/v1Publisher landing page
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-
https://www.researchsquare.com/article/rs-2381460/latest.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
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https://www.researchsquare.com/article/rs-2381460/latest.pdfDirect OA link when available
- Concepts
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Artificial intelligence, Computer vision, Computer science, Position (finance), Binocular vision, Ranging, Feature (linguistics), RGB color model, Pixel, Stereo camera, Economics, Telecommunications, Linguistics, Finance, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.acquired | 112, 166 |
| abstract_inverted_index.datasets | 151 |
| abstract_inverted_index.improved | 200 |
| abstract_inverted_index.obtained | 79, 129, 262 |
| abstract_inverted_index.original | 220 |
| abstract_inverted_index.position | 102, 117, 127 |
| abstract_inverted_index.relative | 116 |
| abstract_inverted_index.reliably | 265 |
| abstract_inverted_index.results; | 158 |
| abstract_inverted_index.targets, | 29 |
| abstract_inverted_index.valuable | 268 |
| abstract_inverted_index.according | 154 |
| abstract_inverted_index.accuracy, | 144 |
| abstract_inverted_index.algorithm | 39, 98, 202, 221 |
| abstract_inverted_index.binocular | 73, 82, 132, 169, 196 |
| abstract_inverted_index.combining | 124, 194 |
| abstract_inverted_index.corrected | 172 |
| abstract_inverted_index.detection | 33, 143, 157, 182, 191, 213, 233, 239, 253 |
| abstract_inverted_index.equipment | 45 |
| abstract_inverted_index.proposed, | 41 |
| abstract_inverted_index.real-time | 50 |
| abstract_inverted_index.autonomous | 271 |
| abstract_inverted_index.calculated | 122 |
| abstract_inverted_index.distortion | 174 |
| abstract_inverted_index.evaluation | 223 |
| abstract_inverted_index.navigating | 17 |
| abstract_inverted_index.retrained; | 163 |
| abstract_inverted_index.acquisition | 67 |
| abstract_inverted_index.correction, | 177 |
| abstract_inverted_index.information | 103, 128 |
| abstract_inverted_index.intelligent | 8, 272 |
| abstract_inverted_index.recognition | 54 |
| abstract_inverted_index.environment. | 61, 279 |
| abstract_inverted_index.experimental | 44, 186 |
| abstract_inverted_index.high-precision | 52 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/6 |
| sustainable_development_goals[0].score | 0.6800000071525574 |
| sustainable_development_goals[0].display_name | Clean water and sanitation |
| citation_normalized_percentile.value | 0.07514947 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |