AIDOVECL: AI-generated Dataset of Outpainted Vehicles for Eye-level Classification and Localization

Amir Kazemi, Qurat ul ain Fatima, Volodymyr Kindratenko, Christopher W Tessum

Abstract: Image labeling is a critical bottleneck in the development of computer vision technologies, often constraining machine learning performance due to the time-intensive nature of manual annotations. This work introduces a novel approach that leverages outpainting to mitigate annotated data scarcity by generating artificial contexts and annotations, significantly reducing labeling efforts. We apply this technique to a particularly acute challenge in autonomous driving, urban planning, and environmental monitoring: the lack of diverse, eye-level vehicle images from desired classes. Our dataset comprises AI-generated vehicle images obtained by detecting and cropping vehicles from manually selected seed images, which are then outpainted onto larger canvases to simulate varied real-world conditions. The outpainted images include detailed annotations, providing high-quality ground truth data. Advanced outpainting techniques and image quality assessments ensure visual fidelity and contextual relevance. Ablation results show that incorporating AIDOVECL improves overall detection performance by up to about 10%, and delivers gains of up to about 40% in settings with greater diversity of context, object scale, and placement, with underrepresented classes achieving up to about 50% higher true positives. AIDOVECL enhances vehicle detection by augmenting real training data and supporting evaluation across diverse scenarios. By demonstrating outpainting as an automatic annotation paradigm, it offers a practical and versatile solution for building fine-grained datasets with reduced labeling effort across multiple machine learning domains. The code and links to datasets are available for further research and replication at https://github.com/amir-kazemi/aidovecl.

Keywords: Generative AI, Prompt-Guided Diffusion, Inpainting, Object Detection.

Previous DMLR Submission Url: https://openreview.net/forum?id=59MARvj9vN

Changes Since Last Submission:

In this minor revision, we:
- polished the presentation;
- elaborated on the pipeline and dataset contributions in the **Dataset Availability and User Framework** section, added after the **Conclusion**, and provided links to the code and dataset, along with the dataset DOI, in that section; and
- reorganized several sections based on the suggestions, including moving **Limitations** and **Future Work** before the Conclusion.

Code: https://github.com/amir-kazemi/aidovecl

Assigned Action Editor: ~Sergio_Escalera1

Submission Number: 87