Image labeling is a critical bottleneck in the development of computer vision technologies, often constraining the potential of machine learning models due to the time-intensive nature of manual annotations. This work introduces a novel approach that leverages outpainting to address the problem of annotated data scarcity by generating artificial contexts and annotations, significantly reducing manual 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 in 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. Augmentation with outpainted vehicles improves overall performance metrics by up to 8\% and enhances prediction of underrepresented classes by up to 20\%. This approach, exemplifying outpainting as a self-annotating paradigm, presents a solution that enhances dataset versatility across multiple domains of machine learning. The code and links to datasets used in this study are available for further research and replication at https://github.com/amir-kazemi/aidovecl.

本研究解决了计算机视觉技术中图像标注的瓶颈问题，尤其是缺乏多样的眼平视车辆图像的挑战。通过采用出画技术生成人工上下文和标注，显著减少人工标注工作，提高了数据集的多样性和适用性，结果表明增强的数据集在性能指标上提升了最多8%、对欠代表类的预测能力提升了20%。

AI生成的出画车辆数据集用于视角分类和定位