By improving YOLOv5s and SIFT for cartoon characters Detection methods to prevent infringement

In recent years, with the popularity of the Internet, a large number of copyright infringement cases continue to emerge. Especially in the cartoon industry, due to the difficulty of replicating original works, infringement incidents are more frequent. Therefore, this article aims to prevent cartoon character infringement and designs and implements a cartoon character detection method based on improved YOLOv5s and SIFT algorithms. In response to the weak recognition ability of YOLOv5s model for small-sized objects, the YOLOv5s algorithm is improved by introducing multi-scale prediction, adding new categories, adjusting parameters, and adjusting weights. The experimental results show that the improved YOLOv5s and SIFT algorithms have improved accuracy, recall, and F1 score, and significantly improved computational efficiency when processing large-scale images.

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Publication Details

Journal
Science and Technology of Engineering Chemistry and Environmental Protection
Published
2025-04-21
DOI
https://doi.org/10.61173/h9d5fz46
Primary Topic
Handwritten Text Recognition Techniques
Type
article
Field-Weighted Citation Impact
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article

By improving YOLOv5s and SIFT for cartoon characters Detection methods to prevent infringement

Zhenyuan Qin
Science and Technology of Engineering Chemistry and Environmental Protection
Handwritten Text Recognition Techniques
article

By improving YOLOv5s and SIFT for cartoon characters Detection methods to prevent infringement

Zhenyuan Qin
article en

Abstract

In recent years, with the popularity of the Internet, a large number of copyright infringement cases continue to emerge. Especially in the cartoon industry, due to the difficulty of replicating original works, infringement incidents are more frequent. Therefore, this article aims to prevent cartoon character infringement and designs and implements a cartoon character detection method based on improved YOLOv5s and SIFT algorithms. In response to the weak recognition ability of YOLOv5s model for small-sized objects, the YOLOv5s algorithm is improved by introducing multi-scale prediction, adding new categories, adjusting parameters, and adjusting weights. The experimental results show that the improved YOLOv5s and SIFT algorithms have improved accuracy, recall, and F1 score, and significantly improved computational efficiency when processing large-scale images.

Science and Technology of Engineering Chemistry and Environmental ProtectionVol. 1(2)
Xi'an International Studies University (CN), Xi’an International University (CN)
Openalex Percentile: Top 94%
Handwritten Text Recognition Techniques
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