论文标题
FCO：一个简单且坚固的无锚对象检测器
FCOS: A simple and strong anchor-free object detector
论文作者
论文摘要
在计算机视觉中，对象检测是最重要的任务之一，它是一些实例级识别任务和许多下游应用程序的基础。最近，一阶段方法由于设计和竞争性能的简单性能而引起了两阶段方法的关注。在这里，我们提出了一个完全卷积的一阶段对象检测器（FCO），以每像素预测的方式求解对象检测，类似于其他密集的预测问题，例如语义分割。几乎所有最先进的对象探测器，例如视网膜，SSD，Yolov3和更快的R-CNN都依赖于预定的锚盒。相比之下，我们提出的检测器FCO不含锚点，也没有提案。通过消除预定义的锚箱集，FCO完全避免了与锚箱相关的复杂计算，例如在训练过程中计算联合（IOU）分数的相交。更重要的是，我们还避免了与锚盒有关的所有超参数，这些参数通常对最终检测性能敏感。借助唯一的后处理非最大抑制（NMS），我们展示了一个更简单，灵活的检测框架，可提高检测精度。我们希望所提出的FCO框架可以作为许多其他实例级任务的简单而强大的替代方案。代码和预训练模型可在以下网址找到：https：//git.io/adelaidet
In computer vision, object detection is one of most important tasks, which underpins a few instance-level recognition tasks and many downstream applications. Recently one-stage methods have gained much attention over two-stage approaches due to their simpler design and competitive performance. Here we propose a fully convolutional one-stage object detector (FCOS) to solve object detection in a per-pixel prediction fashion, analogue to other dense prediction problems such as semantic segmentation. Almost all state-of-the-art object detectors such as RetinaNet, SSD, YOLOv3, and Faster R-CNN rely on pre-defined anchor boxes. In contrast, our proposed detector FCOS is anchor box free, as well as proposal free. By eliminating the pre-defined set of anchor boxes, FCOS completely avoids the complicated computation related to anchor boxes such as calculating the intersection over union (IoU) scores during training. More importantly, we also avoid all hyper-parameters related to anchor boxes, which are often sensitive to the final detection performance. With the only post-processing non-maximum suppression (NMS), we demonstrate a much simpler and flexible detection framework achieving improved detection accuracy. We hope that the proposed FCOS framework can serve as a simple and strong alternative for many other instance-level tasks. Code and pre-trained models are available at: https://git.io/AdelaiDet
