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.

该研究提出了一种新的 Deformable Polar Polygon Object Detection 方法，使用带有灵活顶点的稀疏多边形表示来实现精确形状对齐和低计算成本，通过稠密重新采样训练和预测多边形，实现高精度的物体形状学习，并成功应用于自动驾驶中的各种物体检测任务。