论文标题
无锚点，两阶段对象检测的转角建议网络
Corner Proposal Network for Anchor-free, Two-stage Object Detection
论文作者
论文摘要
对象检测的目的是确定图像中对象的类和位置。本文提出了一个新颖的无锚，两阶段的框架，该框架首先通过找到潜在的角关键组合，然后通过独立的分类阶段将类标签分配给每个建议。我们证明，这两个阶段分别是改善召回和精度的有效解决方案，并且可以将它们集成到端到端网络中。我们的方法称为角落提案网络（CPN），具有检测各种尺度对象的能力，也避免被大量的假阳性建议所混淆。在MS-COCO数据集上，CPN的AP为49.2％，在最新的对象检测方法之间具有竞争力。 CPN还符合计算效率的方案，在26.2/43.3 fps时，AP的AP为41.6％/39.7％，超过了以相同推理速度的大多数竞争对手。代码可从https://github.com/duankaiwen/cpndet获得
The goal of object detection is to determine the class and location of objects in an image. This paper proposes a novel anchor-free, two-stage framework which first extracts a number of object proposals by finding potential corner keypoint combinations and then assigns a class label to each proposal by a standalone classification stage. We demonstrate that these two stages are effective solutions for improving recall and precision, respectively, and they can be integrated into an end-to-end network. Our approach, dubbed Corner Proposal Network (CPN), enjoys the ability to detect objects of various scales and also avoids being confused by a large number of false-positive proposals. On the MS-COCO dataset, CPN achieves an AP of 49.2% which is competitive among state-of-the-art object detection methods. CPN also fits the scenario of computational efficiency, which achieves an AP of 41.6%/39.7% at 26.2/43.3 FPS, surpassing most competitors with the same inference speed. Code is available at https://github.com/Duankaiwen/CPNDet
