论文标题
SCR：几何先验的平滑轮廓回归
SCR: Smooth Contour Regression with Geometric Priors
论文作者
论文摘要
尽管传统上使用像素级掩码或边界框，但对象检测方法最近出现了诸如多边形或主动轮廓之类的替代表示。其中，基于傅立叶或Chebyshev系数的回归方法的方法在自由形对象上显示出很高的潜力。通过将对象形状定义为极性函数，它们仅限于星形域。我们使用SCR：一种捕获无分辨率对象轮廓作为复杂周期函数的方法来解决此问题。由于设计有效的几何形状先验，该方法在准确性和紧凑度之间提供了良好的折衷。我们在流行的COCO 2017实例细分数据集上进行基准SCR，并显示其针对该领域现有算法的竞争力。此外，我们设计了一个紧凑的网络版本，我们在具有广泛的功率目标的嵌入式硬件上基准测试了该版本，从而实现了实时性能。
While object detection methods traditionally make use of pixel-level masks or bounding boxes, alternative representations such as polygons or active contours have recently emerged. Among them, methods based on the regression of Fourier or Chebyshev coefficients have shown high potential on freeform objects. By defining object shapes as polar functions, they are however limited to star-shaped domains. We address this issue with SCR: a method that captures resolution-free object contours as complex periodic functions. The method offers a good compromise between accuracy and compactness thanks to the design of efficient geometric shape priors. We benchmark SCR on the popular COCO 2017 instance segmentation dataset, and show its competitiveness against existing algorithms in the field. In addition, we design a compact version of our network, which we benchmark on embedded hardware with a wide range of power targets, achieving up to real-time performance.
