Power equipment image enhancement processing based on YOLO-v8 target detection model under MSRCR algorithm.
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| Title: | Power equipment image enhancement processing based on YOLO-v8 target detection model under MSRCR algorithm. |
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| Authors: | Zhou, Guoliang1 (AUTHOR), Liu, Min2 (AUTHOR), Wang, Hongxu1 (AUTHOR), Zheng, Yi1 (AUTHOR) |
| Source: | International Journal of Low Carbon Technologies. 2024, Vol. 19, p1717-1724. 8p. |
| Subjects: | Image processing equipment, Image intensifiers, Algorithms, Image enhancement (Imaging systems) |
| Abstract: | With the rapid development of the power industry, higher requirements have been put forward for real-time monitoring and fault identification of power equipment. However, images of power equipment in actual scenes are often affected by problems such as uneven illumination and color distortion, leading to a decrease in the performance of the target detection model. Hence, this paper suggests merging the Multi-Scale Retinex with Color Restoration (MSRCR) algorithm with the YOLO-v8 target detection model to enhance the visual quality of power equipment images and boost the accuracy and efficiency of target detection. Initially, the MSRCR algorithm enhances image brightness, contrast, and color restoration and preserves edge and detail features. Subsequently, the paper explores the architecture of YOLO-v8, incorporating the SE (Squeeze-and-Excitation) attention mechanism. This mechanism dynamically adjusts channel weights to optimize feature processing in input data. The final experimental results show that using the MSRCR algorithm to enhance the data and combining it with the SE attention mechanism have improved by about 3.2% compared to the original YOLO-v8 model. In comparative experiments with other algorithms, the method proposed in this article achieved an accuracy of 94.3% and a recall rate of 92.6%, which are both higher than other models. By enhancing power equipment images with the MSRCR algorithm, the YOLO-v8 model has significantly improved both target detection accuracy and recall rate. In summary, the MSRCR power equipment image enhancement processing method proposed in this article based on the YOLO-v8 target detection model can effectively improve the visual quality of power equipment images and improve the accuracy and efficiency of target detection. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Low Carbon Technologies is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 182369815 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Power equipment image enhancement processing based on YOLO-v8 target detection model under MSRCR algorithm. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Zhou%2C+Guoliang%22">Zhou, Guoliang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liu%2C+Min%22">Liu, Min</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Hongxu%22">Wang, Hongxu</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zheng%2C+Yi%22">Zheng, Yi</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Low+Carbon+Technologies%22">International Journal of Low Carbon Technologies</searchLink>. 2024, Vol. 19, p1717-1724. 8p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Image+processing+equipment%22">Image processing equipment</searchLink><br /><searchLink fieldCode="DE" term="%22Image+intensifiers%22">Image intensifiers</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Image+enhancement+%28Imaging+systems%29%22">Image enhancement (Imaging systems)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: With the rapid development of the power industry, higher requirements have been put forward for real-time monitoring and fault identification of power equipment. However, images of power equipment in actual scenes are often affected by problems such as uneven illumination and color distortion, leading to a decrease in the performance of the target detection model. Hence, this paper suggests merging the Multi-Scale Retinex with Color Restoration (MSRCR) algorithm with the YOLO-v8 target detection model to enhance the visual quality of power equipment images and boost the accuracy and efficiency of target detection. Initially, the MSRCR algorithm enhances image brightness, contrast, and color restoration and preserves edge and detail features. Subsequently, the paper explores the architecture of YOLO-v8, incorporating the SE (Squeeze-and-Excitation) attention mechanism. This mechanism dynamically adjusts channel weights to optimize feature processing in input data. The final experimental results show that using the MSRCR algorithm to enhance the data and combining it with the SE attention mechanism have improved by about 3.2% compared to the original YOLO-v8 model. In comparative experiments with other algorithms, the method proposed in this article achieved an accuracy of 94.3% and a recall rate of 92.6%, which are both higher than other models. By enhancing power equipment images with the MSRCR algorithm, the YOLO-v8 model has significantly improved both target detection accuracy and recall rate. In summary, the MSRCR power equipment image enhancement processing method proposed in this article based on the YOLO-v8 target detection model can effectively improve the visual quality of power equipment images and improve the accuracy and efficiency of target detection. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Low Carbon Technologies is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1093/ijlct/ctae122 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 8 StartPage: 1717 Subjects: – SubjectFull: Image processing equipment Type: general – SubjectFull: Image intensifiers Type: general – SubjectFull: Algorithms Type: general – SubjectFull: Image enhancement (Imaging systems) Type: general Titles: – TitleFull: Power equipment image enhancement processing based on YOLO-v8 target detection model under MSRCR algorithm. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Zhou, Guoliang – PersonEntity: Name: NameFull: Liu, Min – PersonEntity: Name: NameFull: Wang, Hongxu – PersonEntity: Name: NameFull: Zheng, Yi IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: 2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 17481317 Numbering: – Type: volume Value: 19 Titles: – TitleFull: International Journal of Low Carbon Technologies Type: main |
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