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Related Experiment Video

Updated: Aug 27, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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Ghostformer: A GhostNet-Based Two-Stage Transformer for Small Object Detection.

Sijia Li1, Furkat Sultonov1, Jamshid Tursunboev1

  • 1Department of Artificial Intelligence, Kyungpook National University, Daegu 41566, Korea.

Sensors (Basel, Switzerland)
|September 23, 2022
PubMed
Summary

This study introduces an enhanced two-stage transformer model using GhostNet for improved small object detection. The novel approach refines object queries, significantly boosting accuracy in identifying small targets.

Keywords:
GhostNetregional proposalssmall object detectiontwo-stage transformer

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Area of Science:

  • Computer Vision
  • Deep Learning
  • Artificial Intelligence

Background:

  • Small object detection remains a challenging task in computer vision.
  • Existing methods like deformable DETR struggle with efficiency and accuracy for small objects.

Purpose of the Study:

  • To propose a novel two-stage transformer model integrated with GhostNet.
  • To enhance the performance of small object detection by optimizing feature extraction and query refinement.

Main Methods:

  • Utilized GhostNet as a backbone for efficient feature extraction.
  • Implemented a two-stage detection process with refined object queries.
  • Modified the decoder layer to optimize target accuracy.

Main Results:

  • The proposed model achieved higher average precision (AP) for small object detection compared to the original deformable DETR.
  • Demonstrated improved performance on the COCO 2017 dataset.

Conclusions:

  • The novel two-stage transformer with GhostNet offers a superior approach for small object detection.
  • The method provides a more efficient and accurate solution for identifying small objects in complex scenes.