A Survey of Deep Learning-Based Object Detection
A Survey of Deep Learning-Based Object Detection
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DOI：
10.1109/access.2019.2939201
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发表时间：
2019-01-01
期刊：
影响因子：
3.9
通讯作者：
Qu, Rong
中科院分区：
文献类型：
--
作者：
Jiao, Licheng;Zhang, Fan;Qu, Rong
目标检测是计算机视觉中最重要和最具挑战性的分支之一，它被广泛应用于人们的生活中，如监控安全、自动驾驶等，目的是定位某一类语义对象的实例。随着用于检测任务的深度学习算法的快速发展，目标检测器的性能得到了极大的提高。为了全面、深入地了解目标检测流水线的主要发展现状，本文首先分析了现有的典型检测模型的方法，并对基准数据集进行了描述。然后，我们系统地对各种目标检测方法进行了全面的概述，包括一级检测器和两级检测器。此外，我们还列出了传统应用和新应用。分析了目标检测的几个有代表性的分支。最后，我们讨论了利用这些目标检测方法来构建一个有效和高效的系统的架构，并指出了一系列的发展趋势，以便更好地跟踪最新的算法和进一步的研究。
Object detection is one of the most important and challenging branches of computer vision, which has been widely applied in people's life, such as monitoring security, autonomous driving and so on, with the purpose of locating instances of semantic objects of a certain class. With the rapid development of deep learning algorithms for detection tasks, the performance of object detectors has been greatly improved. In order to understand the main development status of object detection pipeline thoroughly and deeply, in this survey, we analyze the methods of existing typical detection models and describe the benchmark datasets at first. Afterwards and primarily, we provide a comprehensive overview of a variety of object detection methods in a systematic manner, covering the one-stage and two-stage detectors. Moreover, we list the traditional and new applications. Some representative branches of object detection are analyzed as well. Finally, we discuss the architecture of exploiting these object detection methods to build an effective and efficient system and point out a set of development trends to better follow the state-of-the-art algorithms and further research.