Related papers: Image compositing is all you need for data augmentation

Image compositing is all you need for data augmentation

URL: http://arxiv.org/abs/2502.13936v1
Date: Wed, 19 Feb 2025 18:24:02 GMT
Title: Image compositing is all you need for data augmentation
Authors: Ang Jia Ning Shermaine, Michalis Lazarou, Tania Stathaki,
Abstract summary: This paper investigates the impact of various data augmentation techniques on the performance of object detection models. We fine-tune the model on a custom dataset consisting of commercial and military aircraft, applying different augmentation strategies.
Score: 6.647179199462945
License:
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.

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