A Comparative Analysis of Object Detection Methods for Robotic Grasping

DOI

説明

The objects grasping is one of the fundamental robotic problems. Accurate and efficient real-time object detection is crucial for successful grasping in robots equipped with monocular vision. Deep machine learning has made significant progress in solving problems of object detection and image segmentation. At the same time, classical computer vision methods do not lose their relevance and can also be used for these tasks. In this research, we conduct a comparative analysis of the effectiveness the YOLOv8-seg neural network model versions for solving the image segmentation problem with classical segmentation methods. The obtained results allowed us to formulate some recommendations on the choice of a particular method for object detection depending on the surrounding environment conditions.

収録刊行物

キーワード

詳細情報 詳細情報について

  • CRID
    1390862931515291648
  • DOI
    10.5954/icarob.2024.os11-3
  • ISSN
    21887829
  • 本文言語コード
    en
  • データソース種別
    • JaLC
    • Crossref
  • 抄録ライセンスフラグ
    使用不可

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