Manuscript received June 13, 2024; revised June 24, 2024; accepted July 15, 2024; published March 14, 2025.

Abstract—Many machine learning applications are constrained by limited quantity, quality, and variance of the collected real-world datasets used for training and evaluation. In this work, authors leveraged generative artificial intelligence techniques to extend the amount of data available to train and evaluate Convolutional Neural Networks (CNNs) for object detection and classification. Stable Diffusion was used as the core augmentation algorithm alongside traditional machine vision techniques. A variety of augmentation techniques were compared in terms of their impact on training and evaluating CNNs. The augmented images were used in part to train CNNs to improve the performance of detection models when evaluated on real-world images. Additional experiments were conducted which quantified the prediction performance on real-world data by measuring the performance on similar synthetic data. Within these experiments, various ratios of synthetic and real-world images were used to train networks which were then evaluated on real-world and synthetic holdout datasets.

Keywords—computer vision, synthetic data, generative models, validation and verification, neural style transfer

Cite: Ryan P. O’Shea, Gurpreet Singh, Ari B. Goodman, Thomas J. Keane, Michael A. Brenner, Christopher J. Jaworowski, Tushar A. Patel, and James T. Hing, "Comparison of Augmentation Techniques with Stable Diffusion for Aircraft Identification," Journal of Image and Graphics, Vol. 13, No. 2, pp. 151-157, 2025.

Copyright © 2025 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC-BY-4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.