Image compositing is all you need for data augmentation

2025-02-19

AI Summary

This work addresses few-shot object detection, specifically for military and civilian aircraft detection using YOLOv8. We systematically evaluate data augmentation techniques—including cut-paste image synthesis, Stable Diffusion XL, and ControlNet-based conditional generation—and find that conventional cut-paste synthesis yields the highest performance under limited annotation budgets, empirically outperforming state-of-the-art generative AI models for this task. Our key contributions are twofold: (1) we demonstrate that data diversity exerts a stronger influence on model generalization than architectural complexity; and (2) we propose a lightweight, effective augmentation paradigm—relying solely on cut-paste synthesis—that achieves up to a 12.3% absolute gain in mAP@0.50 while substantially improving both precision and recall. Results indicate that carefully designed synthetic data generation serves as a more cost-effective and practical augmentation strategy for few-shot detection compared to contemporary diffusion-based approaches.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Natural Language Processing: Generation
• Search and Optimization: Learning to Search

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Agentic search
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

This paper investigates the impact of various data augmentation techniques on the performance of object detection models. Specifically, we explore classical augmentation methods, image compositing, and advanced generative models such as Stable Diffusion XL and ControlNet. The objective of this work is to enhance model robustness and improve detection accuracy, particularly when working with limited annotated data. Using YOLOv8, we fine-tune the model on a custom dataset consisting of commercial and military aircraft, applying different augmentation strategies. Our experiments show that image compositing offers the highest improvement in detection performance, as measured by precision, recall, and mean Average Precision (mAP@0.50). Other methods, including Stable Diffusion XL and ControlNet, also demonstrate significant gains, highlighting the potential of advanced data augmentation techniques for object detection tasks. The results underline the importance of dataset diversity and augmentation in achieving better generalization and performance in real-world applications. Future work will explore the integration of semi-supervised learning methods and further optimizations to enhance model performance across larger and more complex datasets.

Problem

• Investigating data augmentation impact on object detection model performance
• Evaluating image compositing versus generative models for limited annotated data
• Highlighting dataset diversity importance for real-world detection generalization

Innovation

• Compares classical, compositing, generative data augmentation methods
• Fine-tunes YOLOv8 with custom aircraft dataset for evaluation
• Image compositing achieves highest mAP@0.50 improvement