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 mitigate 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. Ablation results show that incorporating AIDOVECL improves overall detection performance by up to 10%, and delivers gains of up to 40% in settings with greater diversity of context, object scale, and placement, with underrepresented classes achieving up to 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 used in this study are available for further research and replication at https://github.com/amir-kazemi/aidovecl .
翻译:图像标注是计算机视觉技术发展的关键瓶颈,由于手动标注耗时费力,常常制约机器学习模型的潜力。本研究提出一种新颖方法,利用外绘技术通过生成人工场景与标注来缓解标注数据稀缺问题,从而显著减少手动标注工作量。我们将此技术应用于自动驾驶、城市规划与环境监测中一个尤为突出的挑战:缺乏所需类别中多样化、平视视角的车辆图像。我们的数据集包含AI生成的车辆图像,这些图像通过从手动选取的种子图像中检测并裁剪车辆获得,随后将其外绘至更大的画布上以模拟多样化的真实世界条件。外绘图像包含详细标注,提供了高质量的真实数据。先进的外绘技术与图像质量评估确保了视觉保真度与场景相关性。消融实验结果表明,引入AIDOVECL可将整体检测性能提升高达10%,在场景多样性、物体尺度与位置变化更大的设置中可获得高达40%的性能增益,其中代表性不足的类别真阳性率提升最高达50%。AIDOVECL通过增强真实训练数据并支持多样化场景下的评估,提升了车辆检测性能。通过将外绘技术示范为自动标注范式,本研究为在减少标注工作量的前提下构建跨多个机器学习领域的细粒度数据集,提供了一种实用且通用的解决方案。本研究中使用的代码与数据集链接可在 https://github.com/amir-kazemi/aidovecl 获取,以供进一步研究与复现。