摘要：过去5年以来，目标检测-一种从一系列给定的类别（例如，汽车，飞机，等）中检测出其在图像中的实例的计算机视觉任务吸引了相关研究人员的注意力。这种浓厚的兴趣不仅可以解释这个任务对于许多应用的重要性，还可以解释自深度卷积神经网络（DCNN）出现以来在该领域的显著进步。本文全面地回顾了近年来关于深度CNN目标检测的文献，并对这些最新进展进行了较为深入的阐述。本文不仅涵盖了典型的目标检测结构（SSD、YOLO、Faster-RCNN），而且还讨论了当前社区面临的挑战，并展示了目标检测相关的扩展问题。最后，本文还介绍了相关的公共数据集及最新算法。
Recent Advances in Object Detection in the Age of Deep Convolutional Neural Networks
Shivang Agarwal, Jean Ogier Du Terrail, Frédéric Jurie
Abstract Object detection-the computer vision task dealing with detecting instances of objects of a certain class (e.g., 'car', 'plane', etc.) in images-attracted a lot of attention from the community during the last 5 years. This strong interest can be explained not only by the importance this task has for many applications but also by the phenomenal advances in this area since the arrival of deep convolutional neural networks (DCNN). This article reviews the recent literature on object detection with deep CNN, in a comprehensive way, and provides an in-depth view of these recent advances. The survey covers not only the typical architectures (SSD, YOLO, Faster-RCNN) but also discusses the challenges currently met by the community and goes on to show how the problem of object detection can be extended. This survey also reviews the public datasets and associated state-of-the-art algorithms.
原文链接：https://arxiv.org/abs/1809.03193
