Detection of rare objects (e.g., traffic cones, traffic barrels and traffic warning triangles) is an important perception task to improve the safety of autonomous driving. Training of such models typically requires a large number of annotated data which is expensive and time consuming to obtain. To address the above problem, an emerging approach is to apply data augmentation to automatically generate cost-free training samples. In this work, we propose a systematic study on simple Copy-Paste data augmentation for rare object detection in autonomous driving. Specifically, local adaptive instance-level image transformation is introduced to generate realistic rare object masks from source domain to the target domain. Moreover, traffic scene context is utilized to guide the placement of masks of rare objects. To this end, our data augmentation generates training data with high quality and realistic characteristics by leveraging both local and global consistency. In addition, we build a new dataset named NM10k consisting 10k training images, 4k validation images and the corresponding labels with a diverse range of scenarios in autonomous driving. Experiments on NM10k show that our method achieves promising results on rare object detection. We also present a thorough study to illustrate the effectiveness of our local-adaptive and global constraints based Copy-Paste data augmentation for rare object detection. The data, development kit and more information of NM10k dataset are available online at: \url{https://nullmax-vision.github.io}.
翻译:稀有物体(例如交通控制器、交通桶和交通警告三角形)的检测是提高自主驾驶安全性的重要认知任务。这类模型的培训通常需要大量昂贵和耗时才能获得的附加说明的数据。为了解决上述问题,正在采取的方法是应用数据扩增来自动生成免费培训样本。在这项工作中,我们提议系统研究简单的复制-帕斯特数据扩增,以便在自主驾驶过程中探测稀有物体。具体而言,引入了本地适应性实例级图像转换,从源域到目标域,产生现实的稀有物体遮罩。此外,利用交通场环境来指导稀有物体的遮罩。为此,我们的数据扩增通过利用本地和全球的一致性,生成高质量和现实性的培训数据。此外,我们建立了一个名为NM10k的新数据集,包括10k培训图像、4k验证图像和具有多种自主驱动情景的相应标签。NMM10troduvel实验显示,我们的方法在稀有物体探测上取得了很有希望的结果。我们还进行了一项透彻的研究,以展示我们可获取的当地稳定数据10号数据库和在线数据库的开发效果。