Chinese knot Identification via YOLOv5

Ziyun Song; Hongwu Gan; Zirui Li

doi:10.1117/12.3004141

11 October 2023 Chinese knot Identification via YOLOv5

Ziyun Song, Hongwu Gan, Zirui Li

Proceedings Volume 12800, Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023); 128003L (2023) https://doi.org/10.1117/12.3004141
Event: 6th International Conference on Computer Information Science and Application Technology (CISAT 2023), 2023, Hangzhou, China

Abstract

In this paper, the author collects corresponding pictures with Chinese knots on the Internet and unifies their format as .jpg. After using Colabeler to label the Chinese knot in each picture, import the xml file and the xml file in pascal-voc format respectively. Put the prepared files into the corresponding folders in the yolo-master project to complete the production of the data set. After ensuring that the running environment of yolo-master is normal, adjust the corresponding parameters, run the train.py file and use this data set to train the model for detecting Chinese knots. The authors then test the model on a dataset of images mixed with other objects and obtain corresponding detection results. Finally, the authors analyze the results using metrics such as precision, recall, and F1 score. Furthermore, we discuss the novelty and future development potential of the method for object detection and recognition in detecting classical Chinese artworks.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Ziyun Song, Hongwu Gan, and Zirui Li "Chinese knot Identification via YOLOv5", Proc. SPIE 12800, Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023), 128003L (11 October 2023); https://doi.org/10.1117/12.3004141

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