Image compositing is all you need for data augmentation
This work addresses the problem of improving object detection accuracy and robustness, especially with limited annotated data, for applications in domains like commercial and military aircraft detection, but it is incremental as it compares existing augmentation methods without introducing a new paradigm.
The paper investigated the impact of data augmentation techniques, including classical methods, image compositing, and generative models like Stable Diffusion XL and ControlNet, on object detection performance using YOLOv8 on a custom aircraft dataset, finding that image compositing provided the highest improvement in precision, recall, and mAP@0.50.
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.