论文标题
学习高斯图以检测密集的对象
Learning Gaussian Maps for Dense Object Detection
论文作者
论文摘要
对象检测是计算机视觉研究的著名分支，最近的许多最先进的对象检测算法是在最近的过去引入的，但是这些对象探测器在密集的对象检测方面有多好？在本文中，我们在场景中回顾了常见且高度准确的对象检测方法，这些方法将彼此近距离放置许多相似的外观对象。我们还表明，高斯地图的多任务学习以及分类和边界框回归使我们在基准方面的准确性显着提高。我们在现有的视网膜网络中介绍高斯层和高斯解码器，以在密集场景中提高准确性，其计算成本与视网膜相同。对于基线视网膜，我们显示了MAP中6 \％和5 \％的增益。我们的方法还达到了SKU110K \ cite {sku110k}数据集中的最先准确度的状态。
Object detection is a famous branch of research in computer vision, many state of the art object detection algorithms have been introduced in the recent past, but how good are those object detectors when it comes to dense object detection? In this paper we review common and highly accurate object detection methods on the scenes where numerous similar looking objects are placed in close proximity with each other. We also show that, multi-task learning of gaussian maps along with classification and bounding box regression gives us a significant boost in accuracy over the baseline. We introduce Gaussian Layer and Gaussian Decoder in the existing RetinaNet network for better accuracy in dense scenes, with the same computational cost as the RetinaNet. We show the gain of 6\% and 5\% in mAP with respect to baseline RetinaNet. Our method also achieves the state of the art accuracy on the SKU110K \cite{sku110k} dataset.
