Most successful object detectors are anchor-based, which is difficult to adapt to the diversity of traffic objects. In this paper, we propose a novel anchor-free method, called FII-CenterNet, which introduces the foreground information to eliminate the interference of the complex background information in traffic scenes. The foreground region proposal network segments the foreground based on boxes-induced segmentation annotation, and midground is proposed to provide rich edge information of the objects. In addition to foreground location, scale information is also introduced to improve the regression performance. Extensive experimental results on two public datasets verify the benefits of the introduction of the foreground information, and demonstrate that our FII-CenterNet achieves the state-of-the-art performance in both accuracy and efficiency.

中文翻译：

---

FII-CenterNet：具有前瞻性的无锚探测器，用于交通目标检测

最成功的对象检测器是基于锚的，这很难适应交通对象的多样性。在本文中，我们提出了一种称为FII-CenterNet的新型免锚方法，该方法引入了前景信息，以消除交通场景中复杂背景信息的干扰。前景区域建议网络基于盒子引起的分割注释对前景进行分割，并提出了中地面以提供对象的丰富边缘信息。除了前景位置之外，还引入了比例尺信息以改善回归性能。在两个公共数据集上的大量实验结果验证了引入前景信息的好处，并证明了我们的FII-CenterNet在准确性和效率上均达到了最先进的性能。