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Related Concept Videos

Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

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In scenarios involving parallel transformers with disparate ratings, developing per-unit models requires accommodating off-nominal turns ratios. This situation arises when the selected base voltages are not proportional to the transformer’s voltage ratings. Consider a transformer where the rated voltages are related by the term a. If the chosen voltage bases satisfy a relationship involving term b, term c is defined as the ratio of these bases. This ratio is then substituted into the...
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The Ideal Transformer01:26

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In single-phase two-winding transformers, two windings are coiled around a magnetic core characterized by cross-sectional area A and magnetic permeability μ. A phasor current i1 enters the left winding while i2 exits the right winding, establishing the fundamental working of the transformer through electromagnetic principles.
Ampere's Law forms the basis of understanding the magnetic field within the transformer. It states that the integral of the magnetic field intensity's tangential...
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Related Experiment Video

Updated: Jan 7, 2026

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

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An efficient reparameterized small object detection transformer for thermal infrared images.

Canhao Guo1,2, Peidong Luo1,2, Zhixing Ma1,2

  • 1School of Artificial Intelligence, Shenzhen Technology University, Shenzhen, 518118, China.

Scientific Reports
|December 24, 2025
PubMed
Summary

This study introduces PWL-RTDETR, an efficient Transformer-based framework for small object detection in infrared images. It achieves high accuracy and reduces computational load, making it ideal for real-time deployment on resource-constrained platforms like Unmanned Aerial Vehicles (UAVs).

Keywords:
Infrared small object detectionLightweight modelModel pruningReparameterizationTransformerUAVWavelet convolution

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A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Small object detection in thermal infrared images is challenging due to low contrast and limited texture.
  • Deployment on edge platforms like Unmanned Aerial Vehicles (UAVs) faces computational constraints.

Purpose of the Study:

  • To develop an efficient Transformer-based framework for infrared small object detection.
  • To address computational limitations for real-time deployment on resource-constrained platforms.

Main Methods:

  • Proposed PWL-RTDETR framework featuring a Partial Convolutional Reparameterization Block (PConvRep-Block) for efficient computation.
  • Introduced WTRCSPNeck, a lightweight neck architecture with CNCSPELAN and WTConv for enhanced multi-scale feature aggregation.
  • Implemented Layer-Adaptive Magnitude-based Pruning for model compression and sparsification.

Main Results:

  • PWL-RTDETR demonstrated superior accuracy compared to state-of-the-art models on HIT-UAV and LLVIP infrared datasets.
  • Achieved significant reductions in model parameters and Floating Point Operations (FLOPs).
  • The model proved suitable for real-time applications in resource-constrained environments.

Conclusions:

  • PWL-RTDETR offers an effective solution for accurate and efficient small object detection in infrared imagery.
  • The framework's efficiency and accuracy make it viable for real-time perception tasks on edge devices like UAVs.