Department
Robotics and Mechatronics Engineering
Document Type
Article
Publication Date
11-29-2023
Embargo Period
3-4-2024
Abstract
This review article comprehensively delves into the rapidly evolving field of domain adaptation in computer and robotic vision. It offers a detailed technical analysis of the opportunities and challenges associated with this topic. Domain adaptation methods play a pivotal role in facilitating seamless knowledge transfer and enhancing the generalization capabilities of computer and robotic vision systems. Our methodology involves systematic data collection and preparation, followed by the application of diverse assessment metrics to evaluate the efficacy of domain adaptation strategies. This study assesses the effectiveness and versatility of conventional, deep learning-based, and hybrid domain adaptation techniques within the domains of computer and robotic vision. Through a cross-domain analysis, we scrutinize the performance of these approaches in different contexts, shedding light on their strengths and limitations. The findings gleaned from our evaluation of specific domains and models offer valuable insights for practical applications while reinforcing the validity of the proposed methodologies.
Journal Title
Applied Sciences
Journal ISSN
2076-3417
Volume
13
Issue
23
Digital Object Identifier (DOI)
10.3390/app132312823
Comments
This article received funding through Kennesaw State University's Faculty Open Access Publishing Fund, supported by the KSU Library System and KSU Office of Research.