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Automatic Fashion Item Labeling System Using YOLO and a High-Level Object Detection Model

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2024, 29(11), pp.41-48
  • DOI : 10.9708/jksci.2024.29.11.041
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : September 27, 2024
  • Accepted : October 28, 2024
  • Published : November 29, 2024

Jun-oh Lim 1 Woo-jin Choi 2 Bongjun Choi 1

1동서대학교
2Hong Kong Polytechnic University

Accredited

ABSTRACT

This paper propose an automatic labeling system for fashion items in images by combining one of the object detection models, YOLO(You Only Look Once), with a high-level classification object detection model. After detecting the primary fashion items, TOP and BOTTOM, in an image, the system analysis the bounding boxes of the detected objects and removes redundant or unnecessary bounding boxes through preprocessing to extract bounding boxes with accurate location information. The extracted bounding boxes are compared to the classes defined by the high-level object detection model with coordinate normalization to perform automatic labeling by matcing the input fashion item types. The system's performance was evaluated on 10,000 fashion images and corresponding test data, and 8,192 images were found to be correctly labeled. This demonstrates a significant improvement in efficiency over manual labeling methods, showing the system's practical contribution to large-scale fashion image data processing.

Citation status

* References for papers published after 2023 are currently being built.

This paper was written with support from the National Research Foundation of Korea.