Regular object detection methods output rectangle bounding boxes, which are unable to accurately describe the actual object shapes. Instance segmentation methods output pixel-level labels, which are computationally expensive for real-time applications. Therefore, a polygon representation is needed to achieve precise shape alignment, while retaining low computation cost. We develop a novel Deformable Polar Polygon Object Detection method (DPPD) to detect objects in polygon shapes. In particular, our network predicts, for each object, a sparse set of flexible vertices to construct the polygon, where each vertex is represented by a pair of angle and distance in the Polar coordinate system. To enable training, both ground truth and predicted polygons are densely resampled to have the same number of vertices with equal-spaced raypoints. The resampling operation is fully differentable, allowing gradient back-propagation. Sparse polygon predicton ensures high-speed runtime inference while dense resampling allows the network to learn object shapes with high precision. The polygon detection head is established on top of an anchor-free and NMS-free network architecture. DPPD has been demonstrated successfully in various object detection tasks for autonomous driving such as traffic-sign, crosswalk, vehicle and pedestrian objects.
翻译:常规的目标检测方法输出方形边界框,无法精确地描述实际对象的形状。实例分割方法输出像素级标签,适用于实时应用的计算成本较高。因此,需要一种多边形表示来实现精确的形状对齐,同时保持低计算成本。我们开发了一种新颖的可变形极坐标多边形目标检测方法(DPPD),用于检测多边形形状的对象。具体来说,我们的网络针对每个对象预测一组灵活的顶点来构建多边形,其中每个顶点由极坐标系统中的角度和距离对表示。为了启用训练,地面真实值和预测的多边形都被密集地重新采样,使其具有相同数量的等间隔射线点的顶点。重新采样操作是完全可微的,允许梯度反向传播。稀疏多边形预测确保了高速运行时推断,而密集重新采样允许网络高精度地学习对象的形状。多边形检测头建立在一个无锚点且无非最大抑制的网络架构之上。DPPD已在自动驾驶的各种目标检测任务中得到成功应用,如交通标识、人行横道、车辆和行人对象。