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YOLOv8-based plastic surface inspector with custom labeling for defect detection

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2024, 29(11), pp.11-20
  • DOI : 10.9708/jksci.2024.29.11.011
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : July 31, 2024
  • Accepted : October 29, 2024
  • Published : November 29, 2024

In-Bok Jung 1 Sangmin Suh 1

1강릉원주대학교

Accredited

ABSTRACT

The rapid advancement of society due to industrialization, particularly through mass production enabled by automation, has led to the production of numerous products. However, it is difficult to ensure that all products are manufactured perfectly without defects. Therefore, identifying defects in products during the production process has become crucial. In modern society, detecting defects in various materials is highly valued. This paper focuses on detecting defects in plastic materials, which are among the most widely used and practical materials. In this study, we manually labeled the dataset, creating a dataset consisting of two classes. We utilized the YOLOv8 (You Only Look Once) model, which is capable of object detection, for training. To ensure fair evaluation, k-Fold Cross Validation was performed, resulting in an average F1 Score of 0.95, mAP50 of 0.97, and mAP50-95 of 0.68.

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.