Layered convolutional neural networks for multi-class image classification

Dzmitry Kasinets; Amir K. Saeed; Benjamin A. Johnson; Benjamin M. Rodriguez

doi:10.1117/12.3014054

7 June 2024 Layered convolutional neural networks for multi-class image classification

Dzmitry Kasinets, Amir K. Saeed, Benjamin A. Johnson, Benjamin M. Rodriguez

Proceedings Volume 13034, Real-Time Image Processing and Deep Learning 2024; 130340H (2024) https://doi.org/10.1117/12.3014054
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Abstract

In the context of the advancing digital landscape, there is a discernible demand for robust and defensible methodologies in addressing the challenges in multi-class image classification. The evolution of intelligent systems mandates swift evaluations of environmental variables to facilitate decision-making within an authorized workflow. Recognizing the imperative role of ensemble models, this paper undertakes an exploration into the efficacy of layered Convolutional Neural Network (CNN) architectures for the nuanced task of multi-class image classification, specifically applied to traffic signage recognition in the dynamic context of a moving vehicle. The research methodology employs a YOLO (You Only Look Once) model to establish a comprehensive training and testing dataset. Subsequently, a stratified approach is adopted, leveraging layered CNN architectures to categorize clusters of objects and, ultimately, extrapolate the pertinent speed limit values. Our endeavor aims to elucidate the procedural framework for integrating CNN models, providing insights into their accuracy within the application domain.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Dzmitry Kasinets, Amir K. Saeed, Benjamin A. Johnson, and Benjamin M. Rodriguez "Layered convolutional neural networks for multi-class image classification", Proc. SPIE 13034, Real-Time Image Processing and Deep Learning 2024, 130340H (7 June 2024); https://doi.org/10.1117/12.3014054

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