Research on Chinese traditional opera costume recognition based on improved YOLOv5
Abstract In order to protect the cultural heritage of opera costumes, establish visual labels for opera costumes, accelerate the establishment of a database for opera costumes, and increase the dissemination of opera culture, we propose an improved You Only Look Once (YOLO) v5-based opera costume re...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2023-02-01
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| Series: | Heritage Science |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40494-023-00883-x |
| _version_ | 1797863858185961472 |
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| author | Kaixuan Liu Kai Lin Chun Zhu |
| author_facet | Kaixuan Liu Kai Lin Chun Zhu |
| author_sort | Kaixuan Liu |
| collection | DOAJ |
| description | Abstract In order to protect the cultural heritage of opera costumes, establish visual labels for opera costumes, accelerate the establishment of a database for opera costumes, and increase the dissemination of opera culture, we propose an improved You Only Look Once (YOLO) v5-based opera costume recognition model for opera costumes with a wide range of styles, rich colors, and complex stage environments. By adding Coordinate Attention (CA) mechanism to the backbone of YOLOv5, the network can focus on more interesting information when extracting features; replacing the original feature pyramid module with a weighted bidirectional feature pyramid module in the Neck part to achieve efficient fusion of features; replacing the original loss function GIOU with DIOU to improve the detection accuracy and convergence speed. The average detection accuracy of the improved YOLOv5 model reaches 86.3% and its inference speed reaches 28 ms per frame through experiments on the homemade Chinese costume dataset, which improves the average detection accuracy by 3.1% compared with the original model, and has good robustness in detecting complex scenes such as covered targets, light-colored costumes, cross targets, dense targets and different angles. The model meets the requirements for accuracy and real-time costume recognition in complex theatrical environments. |
| first_indexed | 2024-04-09T22:42:24Z |
| format | Article |
| id | doaj.art-2c63f3782f5f4be786cd272195398b12 |
| institution | Directory Open Access Journal |
| issn | 2050-7445 |
| language | English |
| last_indexed | 2024-04-09T22:42:24Z |
| publishDate | 2023-02-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Heritage Science |
| spelling | doaj.art-2c63f3782f5f4be786cd272195398b122023-03-22T12:01:58ZengSpringerOpenHeritage Science2050-74452023-02-0111111410.1186/s40494-023-00883-xResearch on Chinese traditional opera costume recognition based on improved YOLOv5Kaixuan Liu0Kai Lin1Chun Zhu2School of Fashion and Art Design, Xi’an Polytechnic UniversitySchool of Fashion and Art Design, Xi’an Polytechnic UniversitySchool of Fashion and Art Design, Xi’an Polytechnic UniversityAbstract In order to protect the cultural heritage of opera costumes, establish visual labels for opera costumes, accelerate the establishment of a database for opera costumes, and increase the dissemination of opera culture, we propose an improved You Only Look Once (YOLO) v5-based opera costume recognition model for opera costumes with a wide range of styles, rich colors, and complex stage environments. By adding Coordinate Attention (CA) mechanism to the backbone of YOLOv5, the network can focus on more interesting information when extracting features; replacing the original feature pyramid module with a weighted bidirectional feature pyramid module in the Neck part to achieve efficient fusion of features; replacing the original loss function GIOU with DIOU to improve the detection accuracy and convergence speed. The average detection accuracy of the improved YOLOv5 model reaches 86.3% and its inference speed reaches 28 ms per frame through experiments on the homemade Chinese costume dataset, which improves the average detection accuracy by 3.1% compared with the original model, and has good robustness in detecting complex scenes such as covered targets, light-colored costumes, cross targets, dense targets and different angles. The model meets the requirements for accuracy and real-time costume recognition in complex theatrical environments.https://doi.org/10.1186/s40494-023-00883-xChinese operaOpera costumeCostume image recognitionYOLOv5Machine learning |
| spellingShingle | Kaixuan Liu Kai Lin Chun Zhu Research on Chinese traditional opera costume recognition based on improved YOLOv5 Heritage Science Chinese opera Opera costume Costume image recognition YOLOv5 Machine learning |
| title | Research on Chinese traditional opera costume recognition based on improved YOLOv5 |
| title_full | Research on Chinese traditional opera costume recognition based on improved YOLOv5 |
| title_fullStr | Research on Chinese traditional opera costume recognition based on improved YOLOv5 |
| title_full_unstemmed | Research on Chinese traditional opera costume recognition based on improved YOLOv5 |
| title_short | Research on Chinese traditional opera costume recognition based on improved YOLOv5 |
| title_sort | research on chinese traditional opera costume recognition based on improved yolov5 |
| topic | Chinese opera Opera costume Costume image recognition YOLOv5 Machine learning |
| url | https://doi.org/10.1186/s40494-023-00883-x |
| work_keys_str_mv | AT kaixuanliu researchonchinesetraditionaloperacostumerecognitionbasedonimprovedyolov5 AT kailin researchonchinesetraditionaloperacostumerecognitionbasedonimprovedyolov5 AT chunzhu researchonchinesetraditionaloperacostumerecognitionbasedonimprovedyolov5 |