From Simulation to Reality: Transfer Learning for Automating Pseudo‐Labeling of Real and Infrared Imagery

From Simulation to Reality: Transfer Learning for Automating Pseudo‐Labeling of Real and Infrared Imagery

Training a convolutional neural network (CNN) for real‐world applications is challenging due to the requirement of high‐quality labeled imagery. This study employs pseudo‐labeling and transfer learning, built upon a 6D pose estimation framework. A CNN trained on synthetic images predicts bounding bo...

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Bibliographic Details
Main Authors: Jeffrey Choate, Derek Worth, Scott L. Nykl, Clark Taylor, Brett Borghetti, Christine Schubert Kabban, Ryan Raettig
Format: Article
Language:English
Published: Wiley 2025-05-01
Series:Advanced Intelligent Systems
Subjects:
convolutional neural network
Perspective‐N‐Point algorithm
pseudo‐labeling
real time pose estimation
synthetic imagery generation
transfer learning
Online Access:https://doi.org/10.1002/aisy.202400575
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