论文标题
可见性指导NMS：在拥挤的交通场景中有效提高Amodal对象检测
Visibility Guided NMS: Efficient Boosting of Amodal Object Detection in Crowded Traffic Scenes
论文作者
论文摘要
对象检测是自动驾驶环境感知的重要任务。现代的2D对象检测框架，例如Yolo，SSD或更快的R-CNN预测每个对象的多个边界框，使用非最大抑制（NMS）来抑制除一个边界框以外的所有框架。虽然对象检测本身是完全端到端学习的，并且不需要任何手动参数选择，但是标准NMS由必须手动选择的重叠阈值进行参数。在实践中，这通常会导致标准NMS策略无法在高度相互阻塞的情况下（例如用于停放的汽车或人群。我们的新颖可见性指导NMS（VG-NMS）利用基于像素的和Amodal对象检测范例，并改善了检测性能，尤其是对于很少的计算开销的高度遮挡对象。我们使用Kitti，Viper和Synscapes数据集评估VG-NMS，并表明它的表现优于当前的最新NMS。
Object detection is an important task in environment perception for autonomous driving. Modern 2D object detection frameworks such as Yolo, SSD or Faster R-CNN predict multiple bounding boxes per object that are refined using Non-Maximum-Suppression (NMS) to suppress all but one bounding box. While object detection itself is fully end-to-end learnable and does not require any manual parameter selection, standard NMS is parametrized by an overlap threshold that has to be chosen by hand. In practice, this often leads to an inability of standard NMS strategies to distinguish different objects in crowded scenes in the presence of high mutual occlusion, e.g. for parked cars or crowds of pedestrians. Our novel Visibility Guided NMS (vg-NMS) leverages both pixel-based as well as amodal object detection paradigms and improves the detection performance especially for highly occluded objects with little computational overhead. We evaluate vg-NMS using KITTI, VIPER as well as the Synscapes dataset and show that it outperforms current state-of-the-art NMS.
