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Transformers with Off-Nominal Turns Ratios

Transformers with Off-Nominal Turns Ratios

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...

Types Of Transformers

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Transformers can provide desired voltages to a circuit by modifying the number of turns in the secondary windings.
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However, if this ratio is less than one, the transformer is said to be a step-down...

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

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

Published on: April 21, 2023

Siamese hierarchical feature fusion transformer for efficient tracking.

Jiahai Dai¹, Yunhao Fu¹, Songxin Wang²

¹Department of Electronic Information Engineering, College of Electronic Science and Engineering, Jilin University, Changchun, China.

Frontiers in Neurorobotics

|December 19, 2022

Summary

This study introduces SiamHFFT, a lightweight object tracking model. It effectively handles small object tracking in complex scenarios with high accuracy and speed, suitable for real-world applications.

Keywords:

hierarchical feature lightweight backbone real-time transformer visual tracking

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Published on: December 24, 2014

Area of Science:

Computer Vision
Deep Learning
Object Tracking

Background:

Deep network-based object trackers are computationally expensive for real-time edge applications.
Lightweight networks offer efficiency but often lack sufficient feature representation for complex tracking tasks, especially for small objects.

Purpose of the Study:

To develop an efficient and accurate lightweight object tracking model for complex scenarios.
To improve the discrimination of small objects from backgrounds in real-time tracking.

Main Methods:

Utilized a lightweight backbone for feature extraction at multiple levels.
Designed a hierarchical feature fusion transformer (HFFT) to integrate multi-level features.
Developed a novel end-to-end model named SiamHFFT.

Main Results:

SiamHFFT achieves real-time performance (29 FPS) on CPU for small object tracking.
Demonstrated effectiveness and efficiency on benchmarks like UAV123, LaSOT, VOT2020, and GOT-10k.
Achieved a strong balance between accuracy and speed in challenging tracking scenarios.

Conclusions:

The proposed SiamHFFT model offers a practical solution for real-world small object tracking.
Hierarchical feature fusion effectively enhances feature representation in lightweight trackers.
The model shows significant potential for improving real-world small object tracking performance.